diff --git a/app.py b/app.py
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+import os
+
+import streamlit as st
+from io import BytesIO
+import base64
+from multiprocessing.dummy import Pool
+from PIL import Image, ImageDraw
+
+import torch
+from torchvision import transforms
+
+# sketches
+from streamlit_drawable_canvas import st_canvas
+from PIL import Image, ImageOps
+from torchvision import transforms
+from src.model_LN_prompt import Model
+
+
+import pickle as pkl
+from html import escape
+from huggingface_hub import hf_hub_download,login 
+
+token = os.getenv("HUGGINGFACE_TOKEN")
+
+# Autentica usando el token
+login(token=token)
+
+# Variables
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+HEIGHT = 200
+N_RESULTS = 15
+color = st.get_option("theme.primaryColor")
+if color is None:
+    color = (0, 0, 255)
+else:
+    color = tuple(int(color.lstrip("#")[i: i + 2], 16) for i in (0, 2, 4))
+
+
+@st.cache_resource
+def load():
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    path_images = 'data'
+
+    # Descargar el modelo desde Hugging Face
+    path_model = hf_hub_download(repo_id="CHSTR/Ecommerce", filename="dinov2_ecommerce.ckpt")
+    print(f"Archivo del modelo descargado en: {path_model}")
+
+    # Cargar el modelo
+    model = Model().to(device)
+    model_checkpoint = torch.load(path_model, map_location=device)
+    model.load_state_dict(model_checkpoint['state_dict'])
+    model.eval()
+    print("Modelo cargado exitosamente")
+
+    # Descargar y cargar los embeddings desde Hugging Face
+    embeddings_file = hf_hub_download(repo_id="CHSTR/Ecommerce", filename="ecommerce_demo.pkl")
+    print(f"Archivo de embeddings descargado en: {embeddings_file}")
+
+    embeddings = {
+        0: pkl.load(open(embeddings_file, "rb")),
+        1: pkl.load(open(embeddings_file, "rb"))
+    }
+
+    # Actualizar los paths de las imágenes en los embeddings
+    for i in range(len(embeddings[0])):
+        embeddings[0][i] = (embeddings[0][i][0], path_images + embeddings[0][i][1].split("/images")[-1])
+
+    for i in range(len(embeddings[1])):
+        embeddings[1][i] = (embeddings[1][i][0], path_images + embeddings[1][i][1].split("/images")[-1])
+
+    return model, path_images, embeddings
+
+
+def compute_text_embeddings(sketch):
+    with torch.no_grad():
+        sketch_feat = model(sketch.to(device), dtype='sketch')
+    return sketch_feat
+
+
+def image_search(query, corpus, n_results=N_RESULTS):
+    query_embedding = compute_text_embeddings(query)
+    corpus_id = 0 if corpus == "Unsplash" else 1
+    image_features = torch.tensor(
+        [item[0] for item in embeddings[corpus_id]]).to(device)
+    
+    dot_product = (image_features @ query_embedding.T)[:, 0]
+    _, max_indices = torch.topk(
+        dot_product, n_results, dim=0, largest=True, sorted=True)
+
+    # Diccionario para mapear los paths a labels
+    path_to_label = {path: idx for idx,
+                     (_, path) in enumerate(embeddings[corpus_id])}
+    label_to_path = {idx: path for path, idx in path_to_label.items()}
+    label_of_images = torch.tensor(
+        [path_to_label[item[1]] for item in embeddings[corpus_id]]).to(device)
+
+    return [
+        (
+            # path_images +  "page" + str(i) + ".jpg", # DocExplore
+            label_to_path[i],  # DocExplore
+        )
+        for i in label_of_images[max_indices].cpu().numpy().tolist()
+    ], dot_product[max_indices]  # bbox_of_images[max_indices], dot_product[max_indices]
+
+
+def make_square(img, fill_color=(255, 255, 255)):
+    x, y = img.size
+    size = max(x, y)
+    new_img = Image.new("RGB", (x, y), fill_color)
+    new_img.paste(img)
+    return new_img, x, y
+
+
+@st.cache_data
+def get_images(paths):
+    def process_image(path):
+        return make_square(Image.open(path))
+
+    processed = Pool(N_RESULTS).map(process_image, paths)
+    imgs, xs, ys = [], [], []
+    for img, x, y in processed:
+        imgs.append(img)
+        xs.append(x)
+        ys.append(y)
+    return imgs, xs, ys
+
+
+def convert_pil_to_base64(image):
+    img_buffer = BytesIO()
+    image.save(img_buffer, format="JPEG")
+    byte_data = img_buffer.getvalue()
+    base64_str = base64.b64encode(byte_data)
+    return base64_str
+
+
+def draw_reshape_encode(img, boxes, x, y):
+    boxes = [boxes.tolist()]
+    image = img.copy()
+    draw = ImageDraw.Draw(image)
+    new_x, new_y = int(x * HEIGHT / y), HEIGHT
+    for box in boxes:
+        print("box:", box)
+        draw.rectangle(
+            # (x_min, y_min, x_max, y_max)
+            [(box[0], box[1]), (box[2], box[3])],
+            outline=color,  # Box color
+            width=7  # Box width
+        )
+
+
+def get_html(url_list, encoded_images):
+    html = "<div style='margin-top: 20px; max-width: 1200px; display: flex; flex-wrap: wrap; justify-content: space-evenly'>"
+    for i in range(len(url_list)):
+        title, encoded = url_list[i][0], encoded_images[i]
+        html = (
+            html
+            + f"<img title='{escape(title)}' style='height: {HEIGHT}px; margin: 5px' src='data:image/jpeg;base64,{encoded.decode()}'>"
+        )
+    html += "</div>"
+    return html
+
+
+description = """
+#  Sketch-based Image Retrieval (SBIR)
+"""
+
+div_style = {
+    "display": "flex",
+    "justify-content": "center",
+    "flex-wrap": "wrap",
+}
+
+
+print("Cargando modelos...")
+model, path_images, embeddings = load()
+source = {0: "\Ecommerce", 1: "\nNone"}
+
+stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 5)
+
+dataset_transforms = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+
+def main():
+    st.markdown(
+        """
+        <style>
+        .block-container{
+            max-width: 1200px;
+        }
+        div.row-widget > div{
+            flex-direction: row;
+            display: flex;
+            justify-content: center;
+        }
+        div.row-widget.stRadio > div > label{
+            margin-left: 5px;
+            margin-right: 5px;
+        }
+        .row-widget {
+            margin-top: -25px;
+        }
+        section > div:first-child {
+            padding-top: 30px;
+        }
+        div.appview-container > section:first-child{
+            max-width: 320px;
+        }
+        #MainMenu {
+            visibility: hidden;
+        }
+        .stMarkdown {
+            display: grid;
+            place-items: center;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+    st.sidebar.markdown(description)
+
+    st.title("SBIR App")
+    _, col, _ = st.columns((1, 1, 1))
+    with col:
+        canvas_result = st_canvas(
+            background_color="#eee",
+            stroke_width=stroke_width,
+            update_streamlit=True,
+            height=300,
+            width=300,
+            key="color_annotation_app",
+        )
+
+    _, c, _ = st.columns((1, 3, 1))
+    query = ["koala"]  # c.text_input("", value="koala")
+    corpus = c.radio("", ["Ecommerce"])
+
+    if canvas_result.image_data is not None:
+        draw = Image.fromarray(canvas_result.image_data.astype("uint8"))
+        draw = ImageOps.pad(draw.convert("RGB"), size=(224, 224))
+        draw.save("draw.jpg")
+
+        draw_tensor = transforms.ToTensor()(draw)
+        draw_tensor = transforms.Resize((224, 224))(draw_tensor)
+        draw_tensor = transforms.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )(draw_tensor)
+        draw_tensor = draw_tensor.unsqueeze(0)
+    else:
+        return
+
+    if len(query) > 0:
+        retrieved, dot_product = image_search(draw_tensor, corpus)
+        imgs, xs, ys = get_images([x[0] for x in retrieved])
+        encoded_images = []
+        for image_idx in range(len(imgs)):
+            img0, x, y = imgs[image_idx], xs[image_idx], ys[image_idx]
+
+            new_x, new_y = int(x * HEIGHT / y), HEIGHT
+
+            encoded_images.append(convert_pil_to_base64(
+                img0.resize((new_x, new_y))))
+        st.markdown(get_html(retrieved, encoded_images),
+                    unsafe_allow_html=True)
+
+
+if __name__ == "__main__":
+    main()
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diff --git a/data/valid/videojuegos/fffbe8af8f70c60728fbefa61c6eb09c_6.jpg b/data/valid/videojuegos/fffbe8af8f70c60728fbefa61c6eb09c_6.jpg
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diff --git a/data/valid/zapatos/437f5876071d92546caceed4bf67cbd2.jpg b/data/valid/zapatos/437f5876071d92546caceed4bf67cbd2.jpg
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diff --git a/data/valid/zapatos/437f5876071d92546caceed4bf67cbd2_1.jpg b/data/valid/zapatos/437f5876071d92546caceed4bf67cbd2_1.jpg
new file mode 100755
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fc9216fb375d0f7d3eaae35f2557ce13ae01148c
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,10 @@
+numpy==1.21.5
+omegaconf==2.3.0
+Pillow==11.0.0
+pytorch_lightning==2.4.0
+scipy==1.8.0
+streamlit
+streamlit_drawable_canvas
+torchmetrics
+torchmetrics
+torchvision
diff --git a/src/__pycache__/model_LN_prompt.cpython-310.pyc b/src/__pycache__/model_LN_prompt.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cc32363da75ca7d6fc5c4ab3f83c87aa036bcb7a
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diff --git a/src/__pycache__/options.cpython-310.pyc b/src/__pycache__/options.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..20fde5fe283a707a6a6ead8ce6ce7e6402686493
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diff --git a/src/dinov2/__init__.py b/src/dinov2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae847e46898077fe3d8701b8a181d7b4e3d41cd9
--- /dev/null
+++ b/src/dinov2/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+__version__ = "0.0.1"
diff --git a/src/dinov2/__pycache__/__init__.cpython-310.pyc b/src/dinov2/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f9a9f28e00a60c2c477c3441e4e6eb383d5e7cf2
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diff --git a/src/dinov2/configs/__init__.py b/src/dinov2/configs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..68e0830c62ea19649b6cd2361995f6df309d7640
--- /dev/null
+++ b/src/dinov2/configs/__init__.py
@@ -0,0 +1,22 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import pathlib
+
+from omegaconf import OmegaConf
+
+
+def load_config(config_name: str):
+    config_filename = config_name + ".yaml"
+    return OmegaConf.load(pathlib.Path(__file__).parent.resolve() / config_filename)
+
+
+dinov2_default_config = load_config("ssl_default_config")
+
+
+def load_and_merge_config(config_name: str):
+    default_config = OmegaConf.create(dinov2_default_config)
+    loaded_config = load_config(config_name)
+    return OmegaConf.merge(default_config, loaded_config)
diff --git a/src/dinov2/configs/eval/vitb14_pretrain.yaml b/src/dinov2/configs/eval/vitb14_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..117d0f027ca26cd8ce6c010bb78d5a8fac42c70e
--- /dev/null
+++ b/src/dinov2/configs/eval/vitb14_pretrain.yaml
@@ -0,0 +1,6 @@
+student:
+  arch: vit_base
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/eval/vitb14_reg4_pretrain.yaml b/src/dinov2/configs/eval/vitb14_reg4_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d53edc04a0761b4b35c147d63e04d55c90092c8f
--- /dev/null
+++ b/src/dinov2/configs/eval/vitb14_reg4_pretrain.yaml
@@ -0,0 +1,9 @@
+student:
+  arch: vit_base
+  patch_size: 14
+  num_register_tokens: 4
+  interpolate_antialias: true
+  interpolate_offset: 0.0
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/eval/vitg14_pretrain.yaml b/src/dinov2/configs/eval/vitg14_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a96dd5b117b4d59ee210b65037821f1b3e3f16e3
--- /dev/null
+++ b/src/dinov2/configs/eval/vitg14_pretrain.yaml
@@ -0,0 +1,7 @@
+student:
+  arch: vit_giant2
+  patch_size: 14
+  ffn_layer: swiglufused
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/eval/vitg14_reg4_pretrain.yaml b/src/dinov2/configs/eval/vitg14_reg4_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..15948f8589ea0a6e04717453eb88c18388e7f1b2
--- /dev/null
+++ b/src/dinov2/configs/eval/vitg14_reg4_pretrain.yaml
@@ -0,0 +1,10 @@
+student:
+  arch: vit_giant2
+  patch_size: 14
+  ffn_layer: swiglufused
+  num_register_tokens: 4
+  interpolate_antialias: true
+  interpolate_offset: 0.0
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/eval/vitl14_pretrain.yaml b/src/dinov2/configs/eval/vitl14_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a984548bd034f762d455419d7193917fa462dd8
--- /dev/null
+++ b/src/dinov2/configs/eval/vitl14_pretrain.yaml
@@ -0,0 +1,6 @@
+student:
+  arch: vit_large
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/eval/vitl14_reg4_pretrain.yaml b/src/dinov2/configs/eval/vitl14_reg4_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0e2bc4e7b24b1a64d0369a24927996d0f184e283
--- /dev/null
+++ b/src/dinov2/configs/eval/vitl14_reg4_pretrain.yaml
@@ -0,0 +1,9 @@
+student:
+  arch: vit_large
+  patch_size: 14
+  num_register_tokens: 4
+  interpolate_antialias: true
+  interpolate_offset: 0.0
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/eval/vits14_pretrain.yaml b/src/dinov2/configs/eval/vits14_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..afbdb4ba14f1c97130a25b579360f4d817cda495
--- /dev/null
+++ b/src/dinov2/configs/eval/vits14_pretrain.yaml
@@ -0,0 +1,6 @@
+student:
+  arch: vit_small
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/eval/vits14_reg4_pretrain.yaml b/src/dinov2/configs/eval/vits14_reg4_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d25fd638389bfba9220792302dc9dbf5d9a2406a
--- /dev/null
+++ b/src/dinov2/configs/eval/vits14_reg4_pretrain.yaml
@@ -0,0 +1,9 @@
+student:
+  arch: vit_small
+  patch_size: 14
+  num_register_tokens: 4
+  interpolate_antialias: true
+  interpolate_offset: 0.0
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/ssl_default_config.yaml b/src/dinov2/configs/ssl_default_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ccaae1c3174b21bcaf6e803dc861492261e5abe1
--- /dev/null
+++ b/src/dinov2/configs/ssl_default_config.yaml
@@ -0,0 +1,118 @@
+MODEL:
+  WEIGHTS: ''
+compute_precision:
+  grad_scaler: true
+  teacher:
+    backbone:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    dino_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    ibot_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+  student:
+    backbone:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    dino_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp32
+        buffer_dtype: fp32
+    ibot_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp32
+        buffer_dtype: fp32
+dino:
+  loss_weight: 1.0
+  head_n_prototypes: 65536
+  head_bottleneck_dim: 256
+  head_nlayers: 3
+  head_hidden_dim: 2048
+  koleo_loss_weight: 0.1
+ibot:
+  loss_weight: 1.0
+  mask_sample_probability: 0.5
+  mask_ratio_min_max:
+  - 0.1
+  - 0.5
+  separate_head: false
+  head_n_prototypes: 65536
+  head_bottleneck_dim: 256
+  head_nlayers: 3
+  head_hidden_dim: 2048
+train:
+  batch_size_per_gpu: 64
+  dataset_path: ImageNet:split=TRAIN
+  output_dir: .
+  saveckp_freq: 20
+  seed: 0
+  num_workers: 10
+  OFFICIAL_EPOCH_LENGTH: 1250
+  cache_dataset: true
+  centering: "centering" # or "sinkhorn_knopp"
+student:
+  arch: vit_large
+  patch_size: 16
+  drop_path_rate: 0.3
+  layerscale: 1.0e-05
+  drop_path_uniform: true
+  pretrained_weights: ''
+  ffn_layer: "mlp"
+  block_chunks: 0
+  qkv_bias: true
+  proj_bias: true
+  ffn_bias: true
+  num_register_tokens: 0
+  interpolate_antialias: false
+  interpolate_offset: 0.1
+teacher:
+  momentum_teacher: 0.992
+  final_momentum_teacher: 1
+  warmup_teacher_temp: 0.04
+  teacher_temp: 0.07
+  warmup_teacher_temp_epochs: 30
+optim:
+  epochs: 100
+  weight_decay: 0.04
+  weight_decay_end: 0.4
+  base_lr: 0.004  # learning rate for a batch size of 1024
+  lr: 0.  # will be set after applying scaling rule
+  warmup_epochs: 10
+  min_lr: 1.0e-06
+  clip_grad: 3.0
+  freeze_last_layer_epochs: 1
+  scaling_rule: sqrt_wrt_1024
+  patch_embed_lr_mult: 0.2
+  layerwise_decay: 0.9
+  adamw_beta1: 0.9
+  adamw_beta2: 0.999
+crops:
+  global_crops_scale:
+  - 0.32
+  - 1.0
+  local_crops_number: 8
+  local_crops_scale:
+  - 0.05
+  - 0.32
+  global_crops_size: 224
+  local_crops_size: 96
+evaluation:
+  eval_period_iterations: 12500
diff --git a/src/dinov2/configs/train/vitg14.yaml b/src/dinov2/configs/train/vitg14.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d05cf0d59e07ac6e4a2b0f9bdcb6131d7c508962
--- /dev/null
+++ b/src/dinov2/configs/train/vitg14.yaml
@@ -0,0 +1,26 @@
+dino:
+  head_n_prototypes: 131072
+  head_bottleneck_dim: 384
+ibot:
+  separate_head: true
+  head_n_prototypes: 131072
+train:
+  batch_size_per_gpu: 12
+  dataset_path: ImageNet22k
+  centering: sinkhorn_knopp
+student:
+  arch: vit_giant2
+  patch_size: 14
+  drop_path_rate: 0.4
+  ffn_layer: swiglufused
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.994
+optim:
+  epochs: 500
+  weight_decay_end: 0.2
+  base_lr: 2.0e-04  # learning rate for a batch size of 1024
+  warmup_epochs: 80
+  layerwise_decay: 1.0
+crops:
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/train/vitl14.yaml b/src/dinov2/configs/train/vitl14.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d9b491dcc6a522c71328fc2933dd0501123c8f6b
--- /dev/null
+++ b/src/dinov2/configs/train/vitl14.yaml
@@ -0,0 +1,26 @@
+dino:
+  head_n_prototypes: 131072
+  head_bottleneck_dim: 384
+ibot:
+  separate_head: true
+  head_n_prototypes: 131072
+train:
+  batch_size_per_gpu: 32
+  dataset_path: ImageNet22k
+  centering: sinkhorn_knopp
+student:
+  arch: vit_large
+  patch_size: 14
+  drop_path_rate: 0.4
+  ffn_layer: swiglufused
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.994
+optim:
+  epochs: 500
+  weight_decay_end: 0.2
+  base_lr: 2.0e-04  # learning rate for a batch size of 1024
+  warmup_epochs: 80
+  layerwise_decay: 1.0
+crops:
+  local_crops_size: 98
\ No newline at end of file
diff --git a/src/dinov2/configs/train/vitl16_short.yaml b/src/dinov2/configs/train/vitl16_short.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3e7e72864c92175a1354142ac1d64da8070d1e5e
--- /dev/null
+++ b/src/dinov2/configs/train/vitl16_short.yaml
@@ -0,0 +1,6 @@
+# this corresponds to the default config
+train:
+  dataset_path: ImageNet:split=TRAIN
+  batch_size_per_gpu: 64
+student:
+  block_chunks: 4
diff --git a/src/dinov2/data/__init__.py b/src/dinov2/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ded47ea63a7b184ff74a040e2c2c514cda273ef
--- /dev/null
+++ b/src/dinov2/data/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .adapters import DatasetWithEnumeratedTargets
+from .loaders import make_data_loader, make_dataset, SamplerType
+from .collate import collate_data_and_cast
+from .masking import MaskingGenerator
+from .augmentations import DataAugmentationDINO
diff --git a/src/dinov2/data/adapters.py b/src/dinov2/data/adapters.py
new file mode 100644
index 0000000000000000000000000000000000000000..2097bad046fb1052267d5f2bb99c798045f00c92
--- /dev/null
+++ b/src/dinov2/data/adapters.py
@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from typing import Any, Tuple
+
+from torch.utils.data import Dataset
+
+
+class DatasetWithEnumeratedTargets(Dataset):
+    def __init__(self, dataset):
+        self._dataset = dataset
+
+    def get_image_data(self, index: int) -> bytes:
+        return self._dataset.get_image_data(index)
+
+    def get_target(self, index: int) -> Tuple[Any, int]:
+        target = self._dataset.get_target(index)
+        return (index, target)
+
+    def __getitem__(self, index: int) -> Tuple[Any, Tuple[Any, int]]:
+        image, target = self._dataset[index]
+        target = index if target is None else target
+        return image, (index, target)
+
+    def __len__(self) -> int:
+        return len(self._dataset)
diff --git a/src/dinov2/data/augmentations.py b/src/dinov2/data/augmentations.py
new file mode 100644
index 0000000000000000000000000000000000000000..05b1eaa942c14f75b88d9e14732e141e8909b0a1
--- /dev/null
+++ b/src/dinov2/data/augmentations.py
@@ -0,0 +1,118 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+
+from torchvision import transforms
+
+from .transforms import (
+    GaussianBlur,
+    make_normalize_transform,
+)
+
+
+logger = logging.getLogger("dinov2")
+
+
+class DataAugmentationDINO(object):
+    def __init__(
+        self,
+        global_crops_scale,
+        local_crops_scale,
+        local_crops_number,
+        global_crops_size=224,
+        local_crops_size=96,
+    ):
+        self.global_crops_scale = global_crops_scale
+        self.local_crops_scale = local_crops_scale
+        self.local_crops_number = local_crops_number
+        self.global_crops_size = global_crops_size
+        self.local_crops_size = local_crops_size
+
+        logger.info("###################################")
+        logger.info("Using data augmentation parameters:")
+        logger.info(f"global_crops_scale: {global_crops_scale}")
+        logger.info(f"local_crops_scale: {local_crops_scale}")
+        logger.info(f"local_crops_number: {local_crops_number}")
+        logger.info(f"global_crops_size: {global_crops_size}")
+        logger.info(f"local_crops_size: {local_crops_size}")
+        logger.info("###################################")
+
+        # random resized crop and flip
+        self.geometric_augmentation_global = transforms.Compose(
+            [
+                transforms.RandomResizedCrop(
+                    global_crops_size, scale=global_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
+                ),
+                transforms.RandomHorizontalFlip(p=0.5),
+            ]
+        )
+
+        self.geometric_augmentation_local = transforms.Compose(
+            [
+                transforms.RandomResizedCrop(
+                    local_crops_size, scale=local_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
+                ),
+                transforms.RandomHorizontalFlip(p=0.5),
+            ]
+        )
+
+        # color distorsions / blurring
+        color_jittering = transforms.Compose(
+            [
+                transforms.RandomApply(
+                    [transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)],
+                    p=0.8,
+                ),
+                transforms.RandomGrayscale(p=0.2),
+            ]
+        )
+
+        global_transfo1_extra = GaussianBlur(p=1.0)
+
+        global_transfo2_extra = transforms.Compose(
+            [
+                GaussianBlur(p=0.1),
+                transforms.RandomSolarize(threshold=128, p=0.2),
+            ]
+        )
+
+        local_transfo_extra = GaussianBlur(p=0.5)
+
+        # normalization
+        self.normalize = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                make_normalize_transform(),
+            ]
+        )
+
+        self.global_transfo1 = transforms.Compose([color_jittering, global_transfo1_extra, self.normalize])
+        self.global_transfo2 = transforms.Compose([color_jittering, global_transfo2_extra, self.normalize])
+        self.local_transfo = transforms.Compose([color_jittering, local_transfo_extra, self.normalize])
+
+    def __call__(self, image):
+        output = {}
+
+        # global crops:
+        im1_base = self.geometric_augmentation_global(image)
+        global_crop_1 = self.global_transfo1(im1_base)
+
+        im2_base = self.geometric_augmentation_global(image)
+        global_crop_2 = self.global_transfo2(im2_base)
+
+        output["global_crops"] = [global_crop_1, global_crop_2]
+
+        # global crops for teacher:
+        output["global_crops_teacher"] = [global_crop_1, global_crop_2]
+
+        # local crops:
+        local_crops = [
+            self.local_transfo(self.geometric_augmentation_local(image)) for _ in range(self.local_crops_number)
+        ]
+        output["local_crops"] = local_crops
+        output["offsets"] = ()
+
+        return output
diff --git a/src/dinov2/data/collate.py b/src/dinov2/data/collate.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3e32f357a76e6f32162cee14cb6ae1665a4827a
--- /dev/null
+++ b/src/dinov2/data/collate.py
@@ -0,0 +1,49 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import random
+
+
+def collate_data_and_cast(samples_list, mask_ratio_tuple, mask_probability, dtype, n_tokens=None, mask_generator=None):
+    # dtype = torch.half  # TODO: Remove
+
+    n_global_crops = len(samples_list[0][0]["global_crops"])
+    n_local_crops = len(samples_list[0][0]["local_crops"])
+
+    collated_global_crops = torch.stack([s[0]["global_crops"][i] for i in range(n_global_crops) for s in samples_list])
+
+    collated_local_crops = torch.stack([s[0]["local_crops"][i] for i in range(n_local_crops) for s in samples_list])
+
+    B = len(collated_global_crops)
+    N = n_tokens
+    n_samples_masked = int(B * mask_probability)
+    probs = torch.linspace(*mask_ratio_tuple, n_samples_masked + 1)
+    upperbound = 0
+    masks_list = []
+    for i in range(0, n_samples_masked):
+        prob_min = probs[i]
+        prob_max = probs[i + 1]
+        masks_list.append(torch.BoolTensor(mask_generator(int(N * random.uniform(prob_min, prob_max)))))
+        upperbound += int(N * prob_max)
+    for i in range(n_samples_masked, B):
+        masks_list.append(torch.BoolTensor(mask_generator(0)))
+
+    random.shuffle(masks_list)
+
+    collated_masks = torch.stack(masks_list).flatten(1)
+    mask_indices_list = collated_masks.flatten().nonzero().flatten()
+
+    masks_weight = (1 / collated_masks.sum(-1).clamp(min=1.0)).unsqueeze(-1).expand_as(collated_masks)[collated_masks]
+
+    return {
+        "collated_global_crops": collated_global_crops.to(dtype),
+        "collated_local_crops": collated_local_crops.to(dtype),
+        "collated_masks": collated_masks,
+        "mask_indices_list": mask_indices_list,
+        "masks_weight": masks_weight,
+        "upperbound": upperbound,
+        "n_masked_patches": torch.full((1,), fill_value=mask_indices_list.shape[0], dtype=torch.long),
+    }
diff --git a/src/dinov2/data/datasets/__init__.py b/src/dinov2/data/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5550fdc5ce16269bc0c28795a389f0182e8bc6c8
--- /dev/null
+++ b/src/dinov2/data/datasets/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .image_net import ImageNet
+from .image_net_22k import ImageNet22k
diff --git a/src/dinov2/data/datasets/decoders.py b/src/dinov2/data/datasets/decoders.py
new file mode 100644
index 0000000000000000000000000000000000000000..3769f7750d94f7e0f7bce281ef3ff186970fc9cd
--- /dev/null
+++ b/src/dinov2/data/datasets/decoders.py
@@ -0,0 +1,31 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from io import BytesIO
+from typing import Any
+
+from PIL import Image
+
+
+class Decoder:
+    def decode(self) -> Any:
+        raise NotImplementedError
+
+
+class ImageDataDecoder(Decoder):
+    def __init__(self, image_data: bytes) -> None:
+        self._image_data = image_data
+
+    def decode(self) -> Image:
+        f = BytesIO(self._image_data)
+        return Image.open(f).convert(mode="RGB")
+
+
+class TargetDecoder(Decoder):
+    def __init__(self, target: Any):
+        self._target = target
+
+    def decode(self) -> Any:
+        return self._target
diff --git a/src/dinov2/data/datasets/extended.py b/src/dinov2/data/datasets/extended.py
new file mode 100644
index 0000000000000000000000000000000000000000..f60b619a3c797823cccfc89e262cdb230f9188f0
--- /dev/null
+++ b/src/dinov2/data/datasets/extended.py
@@ -0,0 +1,38 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from typing import Any, Tuple
+
+from torchvision.datasets import VisionDataset
+
+from .decoders import TargetDecoder, ImageDataDecoder
+
+
+class ExtendedVisionDataset(VisionDataset):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)  # type: ignore
+
+    def get_image_data(self, index: int) -> bytes:
+        raise NotImplementedError
+
+    def get_target(self, index: int) -> Any:
+        raise NotImplementedError
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        try:
+            image_data = self.get_image_data(index)
+            image = ImageDataDecoder(image_data).decode()
+        except Exception as e:
+            raise RuntimeError(f"can not read image for sample {index}") from e
+        target = self.get_target(index)
+        target = TargetDecoder(target).decode()
+
+        if self.transforms is not None:
+            image, target = self.transforms(image, target)
+
+        return image, target
+
+    def __len__(self) -> int:
+        raise NotImplementedError
diff --git a/src/dinov2/data/datasets/image_net.py b/src/dinov2/data/datasets/image_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d08446147986c58360163e468896e994197c657
--- /dev/null
+++ b/src/dinov2/data/datasets/image_net.py
@@ -0,0 +1,290 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import csv
+from enum import Enum
+import logging
+import os
+from typing import Callable, List, Optional, Tuple, Union
+
+import numpy as np
+
+from .extended import ExtendedVisionDataset
+
+
+logger = logging.getLogger("dinov2")
+_Target = int
+
+
+class _Split(Enum):
+    TRAIN = "train"
+    VAL = "val"
+    TEST = "test"  # NOTE: torchvision does not support the test split
+
+    @property
+    def length(self) -> int:
+        split_lengths = {
+            _Split.TRAIN: 1_281_167,
+            _Split.VAL: 50_000,
+            _Split.TEST: 100_000,
+        }
+        return split_lengths[self]
+
+    def get_dirname(self, class_id: Optional[str] = None) -> str:
+        return self.value if class_id is None else os.path.join(self.value, class_id)
+
+    def get_image_relpath(self, actual_index: int, class_id: Optional[str] = None) -> str:
+        dirname = self.get_dirname(class_id)
+        if self == _Split.TRAIN:
+            basename = f"{class_id}_{actual_index}"
+        else:  # self in (_Split.VAL, _Split.TEST):
+            basename = f"ILSVRC2012_{self.value}_{actual_index:08d}"
+        return os.path.join(dirname, basename + ".JPEG")
+
+    def parse_image_relpath(self, image_relpath: str) -> Tuple[str, int]:
+        assert self != _Split.TEST
+        dirname, filename = os.path.split(image_relpath)
+        class_id = os.path.split(dirname)[-1]
+        basename, _ = os.path.splitext(filename)
+        actual_index = int(basename.split("_")[-1])
+        return class_id, actual_index
+
+
+class ImageNet(ExtendedVisionDataset):
+    Target = Union[_Target]
+    Split = Union[_Split]
+
+    def __init__(
+        self,
+        *,
+        split: "ImageNet.Split",
+        root: str,
+        extra: str,
+        transforms: Optional[Callable] = None,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+    ) -> None:
+        super().__init__(root, transforms, transform, target_transform)
+        self._extra_root = extra
+        self._split = split
+
+        self._entries = None
+        self._class_ids = None
+        self._class_names = None
+
+    @property
+    def split(self) -> "ImageNet.Split":
+        return self._split
+
+    def _get_extra_full_path(self, extra_path: str) -> str:
+        return os.path.join(self._extra_root, extra_path)
+
+    def _load_extra(self, extra_path: str) -> np.ndarray:
+        extra_full_path = self._get_extra_full_path(extra_path)
+        return np.load(extra_full_path, mmap_mode="r")
+
+    def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None:
+        extra_full_path = self._get_extra_full_path(extra_path)
+        os.makedirs(self._extra_root, exist_ok=True)
+        np.save(extra_full_path, extra_array)
+
+    @property
+    def _entries_path(self) -> str:
+        return f"entries-{self._split.value.upper()}.npy"
+
+    @property
+    def _class_ids_path(self) -> str:
+        return f"class-ids-{self._split.value.upper()}.npy"
+
+    @property
+    def _class_names_path(self) -> str:
+        return f"class-names-{self._split.value.upper()}.npy"
+
+    def _get_entries(self) -> np.ndarray:
+        if self._entries is None:
+            self._entries = self._load_extra(self._entries_path)
+        assert self._entries is not None
+        return self._entries
+
+    def _get_class_ids(self) -> np.ndarray:
+        if self._split == _Split.TEST:
+            assert False, "Class IDs are not available in TEST split"
+        if self._class_ids is None:
+            self._class_ids = self._load_extra(self._class_ids_path)
+        assert self._class_ids is not None
+        return self._class_ids
+
+    def _get_class_names(self) -> np.ndarray:
+        if self._split == _Split.TEST:
+            assert False, "Class names are not available in TEST split"
+        if self._class_names is None:
+            self._class_names = self._load_extra(self._class_names_path)
+        assert self._class_names is not None
+        return self._class_names
+
+    def find_class_id(self, class_index: int) -> str:
+        class_ids = self._get_class_ids()
+        return str(class_ids[class_index])
+
+    def find_class_name(self, class_index: int) -> str:
+        class_names = self._get_class_names()
+        return str(class_names[class_index])
+
+    def get_image_data(self, index: int) -> bytes:
+        entries = self._get_entries()
+        actual_index = entries[index]["actual_index"]
+
+        class_id = self.get_class_id(index)
+
+        image_relpath = self.split.get_image_relpath(actual_index, class_id)
+        image_full_path = os.path.join(self.root, image_relpath)
+        with open(image_full_path, mode="rb") as f:
+            image_data = f.read()
+        return image_data
+
+    def get_target(self, index: int) -> Optional[Target]:
+        entries = self._get_entries()
+        class_index = entries[index]["class_index"]
+        return None if self.split == _Split.TEST else int(class_index)
+
+    def get_targets(self) -> Optional[np.ndarray]:
+        entries = self._get_entries()
+        return None if self.split == _Split.TEST else entries["class_index"]
+
+    def get_class_id(self, index: int) -> Optional[str]:
+        entries = self._get_entries()
+        class_id = entries[index]["class_id"]
+        return None if self.split == _Split.TEST else str(class_id)
+
+    def get_class_name(self, index: int) -> Optional[str]:
+        entries = self._get_entries()
+        class_name = entries[index]["class_name"]
+        return None if self.split == _Split.TEST else str(class_name)
+
+    def __len__(self) -> int:
+        entries = self._get_entries()
+        assert len(entries) == self.split.length
+        return len(entries)
+
+    def _load_labels(self, labels_path: str) -> List[Tuple[str, str]]:
+        labels_full_path = os.path.join(self.root, labels_path)
+        labels = []
+
+        try:
+            with open(labels_full_path, "r") as f:
+                reader = csv.reader(f)
+                for row in reader:
+                    class_id, class_name = row
+                    labels.append((class_id, class_name))
+        except OSError as e:
+            raise RuntimeError(f'can not read labels file "{labels_full_path}"') from e
+
+        return labels
+
+    def _dump_entries(self) -> None:
+        split = self.split
+        if split == ImageNet.Split.TEST:
+            dataset = None
+            sample_count = split.length
+            max_class_id_length, max_class_name_length = 0, 0
+        else:
+            labels_path = "labels.txt"
+            logger.info(f'loading labels from "{labels_path}"')
+            labels = self._load_labels(labels_path)
+
+            # NOTE: Using torchvision ImageFolder for consistency
+            from torchvision.datasets import ImageFolder
+
+            dataset_root = os.path.join(self.root, split.get_dirname())
+            dataset = ImageFolder(dataset_root)
+            sample_count = len(dataset)
+            max_class_id_length, max_class_name_length = -1, -1
+            for sample in dataset.samples:
+                _, class_index = sample
+                class_id, class_name = labels[class_index]
+                max_class_id_length = max(len(class_id), max_class_id_length)
+                max_class_name_length = max(len(class_name), max_class_name_length)
+
+        dtype = np.dtype(
+            [
+                ("actual_index", "<u4"),
+                ("class_index", "<u4"),
+                ("class_id", f"U{max_class_id_length}"),
+                ("class_name", f"U{max_class_name_length}"),
+            ]
+        )
+        entries_array = np.empty(sample_count, dtype=dtype)
+
+        if split == ImageNet.Split.TEST:
+            old_percent = -1
+            for index in range(sample_count):
+                percent = 100 * (index + 1) // sample_count
+                if percent > old_percent:
+                    logger.info(f"creating entries: {percent}%")
+                    old_percent = percent
+
+                actual_index = index + 1
+                class_index = np.uint32(-1)
+                class_id, class_name = "", ""
+                entries_array[index] = (actual_index, class_index, class_id, class_name)
+        else:
+            class_names = {class_id: class_name for class_id, class_name in labels}
+
+            assert dataset
+            old_percent = -1
+            for index in range(sample_count):
+                percent = 100 * (index + 1) // sample_count
+                if percent > old_percent:
+                    logger.info(f"creating entries: {percent}%")
+                    old_percent = percent
+
+                image_full_path, class_index = dataset.samples[index]
+                image_relpath = os.path.relpath(image_full_path, self.root)
+                class_id, actual_index = split.parse_image_relpath(image_relpath)
+                class_name = class_names[class_id]
+                entries_array[index] = (actual_index, class_index, class_id, class_name)
+
+        logger.info(f'saving entries to "{self._entries_path}"')
+        self._save_extra(entries_array, self._entries_path)
+
+    def _dump_class_ids_and_names(self) -> None:
+        split = self.split
+        if split == ImageNet.Split.TEST:
+            return
+
+        entries_array = self._load_extra(self._entries_path)
+
+        max_class_id_length, max_class_name_length, max_class_index = -1, -1, -1
+        for entry in entries_array:
+            class_index, class_id, class_name = (
+                entry["class_index"],
+                entry["class_id"],
+                entry["class_name"],
+            )
+            max_class_index = max(int(class_index), max_class_index)
+            max_class_id_length = max(len(str(class_id)), max_class_id_length)
+            max_class_name_length = max(len(str(class_name)), max_class_name_length)
+
+        class_count = max_class_index + 1
+        class_ids_array = np.empty(class_count, dtype=f"U{max_class_id_length}")
+        class_names_array = np.empty(class_count, dtype=f"U{max_class_name_length}")
+        for entry in entries_array:
+            class_index, class_id, class_name = (
+                entry["class_index"],
+                entry["class_id"],
+                entry["class_name"],
+            )
+            class_ids_array[class_index] = class_id
+            class_names_array[class_index] = class_name
+
+        logger.info(f'saving class IDs to "{self._class_ids_path}"')
+        self._save_extra(class_ids_array, self._class_ids_path)
+
+        logger.info(f'saving class names to "{self._class_names_path}"')
+        self._save_extra(class_names_array, self._class_names_path)
+
+    def dump_extra(self) -> None:
+        self._dump_entries()
+        self._dump_class_ids_and_names()
diff --git a/src/dinov2/data/datasets/image_net_22k.py b/src/dinov2/data/datasets/image_net_22k.py
new file mode 100644
index 0000000000000000000000000000000000000000..52b36a2c664a7b72e30173b03b4e2aef1cd2fcd9
--- /dev/null
+++ b/src/dinov2/data/datasets/image_net_22k.py
@@ -0,0 +1,302 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+from enum import Enum
+from functools import lru_cache
+from gzip import GzipFile
+from io import BytesIO
+from mmap import ACCESS_READ, mmap
+import os
+from typing import Any, Callable, List, Optional, Set, Tuple
+import warnings
+
+import numpy as np
+
+from .extended import ExtendedVisionDataset
+
+
+_Labels = int
+
+_DEFAULT_MMAP_CACHE_SIZE = 16  # Warning: This can exhaust file descriptors
+
+
+@dataclass
+class _ClassEntry:
+    block_offset: int
+    maybe_filename: Optional[str] = None
+
+
+@dataclass
+class _Entry:
+    class_index: int  # noqa: E701
+    start_offset: int
+    end_offset: int
+    filename: str
+
+
+class _Split(Enum):
+    TRAIN = "train"
+    VAL = "val"
+
+    @property
+    def length(self) -> int:
+        return {
+            _Split.TRAIN: 11_797_647,
+            _Split.VAL: 561_050,
+        }[self]
+
+    def entries_path(self):
+        return f"imagenet21kp_{self.value}.txt"
+
+
+def _get_tarball_path(class_id: str) -> str:
+    return f"{class_id}.tar"
+
+
+def _make_mmap_tarball(tarballs_root: str, mmap_cache_size: int):
+    @lru_cache(maxsize=mmap_cache_size)
+    def _mmap_tarball(class_id: str) -> mmap:
+        tarball_path = _get_tarball_path(class_id)
+        tarball_full_path = os.path.join(tarballs_root, tarball_path)
+        with open(tarball_full_path) as f:
+            return mmap(fileno=f.fileno(), length=0, access=ACCESS_READ)
+
+    return _mmap_tarball
+
+
+class ImageNet22k(ExtendedVisionDataset):
+    _GZIPPED_INDICES: Set[int] = {
+        841_545,
+        1_304_131,
+        2_437_921,
+        2_672_079,
+        2_795_676,
+        2_969_786,
+        6_902_965,
+        6_903_550,
+        6_903_628,
+        7_432_557,
+        7_432_589,
+        7_813_809,
+        8_329_633,
+        10_296_990,
+        10_417_652,
+        10_492_265,
+        10_598_078,
+        10_782_398,
+        10_902_612,
+        11_203_736,
+        11_342_890,
+        11_397_596,
+        11_589_762,
+        11_705_103,
+        12_936_875,
+        13_289_782,
+    }
+    Labels = _Labels
+
+    def __init__(
+        self,
+        *,
+        root: str,
+        extra: str,
+        transforms: Optional[Callable] = None,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        mmap_cache_size: int = _DEFAULT_MMAP_CACHE_SIZE,
+    ) -> None:
+        super().__init__(root, transforms, transform, target_transform)
+        self._extra_root = extra
+
+        entries_path = self._get_entries_path(root)
+        self._entries = self._load_extra(entries_path)
+
+        class_ids_path = self._get_class_ids_path(root)
+        self._class_ids = self._load_extra(class_ids_path)
+
+        self._gzipped_indices = ImageNet22k._GZIPPED_INDICES
+        self._mmap_tarball = _make_mmap_tarball(self._tarballs_root, mmap_cache_size)
+
+    def _get_entries_path(self, root: Optional[str] = None) -> str:
+        return "entries.npy"
+
+    def _get_class_ids_path(self, root: Optional[str] = None) -> str:
+        return "class-ids.npy"
+
+    def _find_class_ids(self, path: str) -> List[str]:
+        class_ids = []
+
+        with os.scandir(path) as entries:
+            for entry in entries:
+                root, ext = os.path.splitext(entry.name)
+                if ext != ".tar":
+                    continue
+                class_ids.append(root)
+
+        return sorted(class_ids)
+
+    def _load_entries_class_ids(self, root: Optional[str] = None) -> Tuple[List[_Entry], List[str]]:
+        root = self.get_root(root)
+        entries: List[_Entry] = []
+        class_ids = self._find_class_ids(root)
+
+        for class_index, class_id in enumerate(class_ids):
+            path = os.path.join(root, "blocks", f"{class_id}.log")
+            class_entries = []
+
+            try:
+                with open(path) as f:
+                    for line in f:
+                        line = line.rstrip()
+                        block, filename = line.split(":")
+                        block_offset = int(block[6:])
+                        filename = filename[1:]
+
+                        maybe_filename = None
+                        if filename != "** Block of NULs **":
+                            maybe_filename = filename
+                            _, ext = os.path.splitext(filename)
+                            # assert ext == ".JPEG"
+
+                        class_entry = _ClassEntry(block_offset, maybe_filename)
+                        class_entries.append(class_entry)
+            except OSError as e:
+                raise RuntimeError(f'can not read blocks file "{path}"') from e
+
+            assert class_entries[-1].maybe_filename is None
+
+            for class_entry1, class_entry2 in zip(class_entries, class_entries[1:]):
+                assert class_entry1.block_offset <= class_entry2.block_offset
+                start_offset = 512 * class_entry1.block_offset
+                end_offset = 512 * class_entry2.block_offset
+                assert class_entry1.maybe_filename is not None
+                filename = class_entry1.maybe_filename
+                entry = _Entry(class_index, start_offset, end_offset, filename)
+                # Skip invalid image files (PIL throws UnidentifiedImageError)
+                if filename == "n06470073_47249.JPEG":
+                    continue
+                entries.append(entry)
+
+        return entries, class_ids
+
+    def _load_extra(self, extra_path: str) -> np.ndarray:
+        extra_root = self._extra_root
+        extra_full_path = os.path.join(extra_root, extra_path)
+        return np.load(extra_full_path, mmap_mode="r")
+
+    def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None:
+        extra_root = self._extra_root
+        extra_full_path = os.path.join(extra_root, extra_path)
+        os.makedirs(extra_root, exist_ok=True)
+        np.save(extra_full_path, extra_array)
+
+    @property
+    def _tarballs_root(self) -> str:
+        return self.root
+
+    def find_class_id(self, class_index: int) -> str:
+        return str(self._class_ids[class_index])
+
+    def get_image_data(self, index: int) -> bytes:
+        entry = self._entries[index]
+        class_id = entry["class_id"]
+        class_mmap = self._mmap_tarball(class_id)
+
+        start_offset, end_offset = entry["start_offset"], entry["end_offset"]
+        try:
+            mapped_data = class_mmap[start_offset:end_offset]
+            data = mapped_data[512:]  # Skip entry header block
+
+            if len(data) >= 2 and tuple(data[:2]) == (0x1F, 0x8B):
+                assert index in self._gzipped_indices, f"unexpected gzip header for sample {index}"
+                with GzipFile(fileobj=BytesIO(data)) as g:
+                    data = g.read()
+        except Exception as e:
+            raise RuntimeError(f"can not retrieve image data for sample {index} " f'from "{class_id}" tarball') from e
+
+        return data
+
+    def get_target(self, index: int) -> Any:
+        return int(self._entries[index]["class_index"])
+
+    def get_targets(self) -> np.ndarray:
+        return self._entries["class_index"]
+
+    def get_class_id(self, index: int) -> str:
+        return str(self._entries[index]["class_id"])
+
+    def get_class_ids(self) -> np.ndarray:
+        return self._entries["class_id"]
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            return super().__getitem__(index)
+
+    def __len__(self) -> int:
+        return len(self._entries)
+
+    def _dump_entries(self, *args, **kwargs) -> None:
+        entries, class_ids = self._load_entries_class_ids(*args, **kwargs)
+
+        max_class_id_length, max_filename_length, max_class_index = -1, -1, -1
+        for entry in entries:
+            class_id = class_ids[entry.class_index]
+            max_class_index = max(entry.class_index, max_class_index)
+            max_class_id_length = max(len(class_id), max_class_id_length)
+            max_filename_length = max(len(entry.filename), max_filename_length)
+
+        dtype = np.dtype(
+            [
+                ("class_index", "<u4"),
+                ("class_id", f"U{max_class_id_length}"),
+                ("start_offset", "<u4"),
+                ("end_offset", "<u4"),
+                ("filename", f"U{max_filename_length}"),
+            ]
+        )
+        sample_count = len(entries)
+        entries_array = np.empty(sample_count, dtype=dtype)
+        for i, entry in enumerate(entries):
+            class_index = entry.class_index
+            class_id = class_ids[class_index]
+            start_offset = entry.start_offset
+            end_offset = entry.end_offset
+            filename = entry.filename
+            entries_array[i] = (
+                class_index,
+                class_id,
+                start_offset,
+                end_offset,
+                filename,
+            )
+
+        entries_path = self._get_entries_path(*args, **kwargs)
+        self._save_extra(entries_array, entries_path)
+
+    def _dump_class_ids(self, *args, **kwargs) -> None:
+        entries_path = self._get_entries_path(*args, **kwargs)
+        entries_array = self._load_extra(entries_path)
+
+        max_class_id_length, max_class_index = -1, -1
+        for entry in entries_array:
+            class_index, class_id = entry["class_index"], entry["class_id"]
+            max_class_index = max(int(class_index), max_class_index)
+            max_class_id_length = max(len(str(class_id)), max_class_id_length)
+
+        class_ids_array = np.empty(max_class_index + 1, dtype=f"U{max_class_id_length}")
+        for entry in entries_array:
+            class_index, class_id = entry["class_index"], entry["class_id"]
+            class_ids_array[class_index] = class_id
+        class_ids_path = self._get_class_ids_path(*args, **kwargs)
+        self._save_extra(class_ids_array, class_ids_path)
+
+    def _dump_extra(self, *args, **kwargs) -> None:
+        self._dump_entries(*args, *kwargs)
+        self._dump_class_ids(*args, *kwargs)
+
+    def dump_extra(self, root: Optional[str] = None) -> None:
+        return self._dump_extra(root)
diff --git a/src/dinov2/data/loaders.py b/src/dinov2/data/loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6a2f0210efa0fa96be764665b5d6792191b1e72
--- /dev/null
+++ b/src/dinov2/data/loaders.py
@@ -0,0 +1,222 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+from enum import Enum
+from typing import Any, Callable, List, Optional, TypeVar
+
+import torch
+from torch.utils.data import Sampler
+
+from .datasets import ImageNet, ImageNet22k
+from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler
+
+
+logger = logging.getLogger("dinov2")
+
+
+class SamplerType(Enum):
+    DISTRIBUTED = 0
+    EPOCH = 1
+    INFINITE = 2
+    SHARDED_INFINITE = 3
+    SHARDED_INFINITE_NEW = 4
+
+
+def _make_bool_str(b: bool) -> str:
+    return "yes" if b else "no"
+
+
+def _make_sample_transform(image_transform: Optional[Callable] = None, target_transform: Optional[Callable] = None):
+    def transform(sample):
+        image, target = sample
+        if image_transform is not None:
+            image = image_transform(image)
+        if target_transform is not None:
+            target = target_transform(target)
+        return image, target
+
+    return transform
+
+
+def _parse_dataset_str(dataset_str: str):
+    tokens = dataset_str.split(":")
+
+    name = tokens[0]
+    kwargs = {}
+
+    for token in tokens[1:]:
+        key, value = token.split("=")
+        assert key in ("root", "extra", "split")
+        kwargs[key] = value
+
+    if name == "ImageNet":
+        class_ = ImageNet
+        if "split" in kwargs:
+            kwargs["split"] = ImageNet.Split[kwargs["split"]]
+    elif name == "ImageNet22k":
+        class_ = ImageNet22k
+    else:
+        raise ValueError(f'Unsupported dataset "{name}"')
+
+    return class_, kwargs
+
+
+def make_dataset(
+    *,
+    dataset_str: str,
+    transform: Optional[Callable] = None,
+    target_transform: Optional[Callable] = None,
+):
+    """
+    Creates a dataset with the specified parameters.
+
+    Args:
+        dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN).
+        transform: A transform to apply to images.
+        target_transform: A transform to apply to targets.
+
+    Returns:
+        The created dataset.
+    """
+    logger.info(f'using dataset: "{dataset_str}"')
+
+    class_, kwargs = _parse_dataset_str(dataset_str)
+    dataset = class_(transform=transform, target_transform=target_transform, **kwargs)
+
+    logger.info(f"# of dataset samples: {len(dataset):,d}")
+
+    # Aggregated datasets do not expose (yet) these attributes, so add them.
+    if not hasattr(dataset, "transform"):
+        setattr(dataset, "transform", transform)
+    if not hasattr(dataset, "target_transform"):
+        setattr(dataset, "target_transform", target_transform)
+
+    return dataset
+
+
+def _make_sampler(
+    *,
+    dataset,
+    type: Optional[SamplerType] = None,
+    shuffle: bool = False,
+    seed: int = 0,
+    size: int = -1,
+    advance: int = 0,
+) -> Optional[Sampler]:
+    sample_count = len(dataset)
+
+    if type == SamplerType.INFINITE:
+        logger.info("sampler: infinite")
+        if size > 0:
+            raise ValueError("sampler size > 0 is invalid")
+        return InfiniteSampler(
+            sample_count=sample_count,
+            shuffle=shuffle,
+            seed=seed,
+            advance=advance,
+        )
+    elif type in (SamplerType.SHARDED_INFINITE, SamplerType.SHARDED_INFINITE_NEW):
+        logger.info("sampler: sharded infinite")
+        if size > 0:
+            raise ValueError("sampler size > 0 is invalid")
+        # TODO: Remove support for old shuffling
+        use_new_shuffle_tensor_slice = type == SamplerType.SHARDED_INFINITE_NEW
+        return ShardedInfiniteSampler(
+            sample_count=sample_count,
+            shuffle=shuffle,
+            seed=seed,
+            advance=advance,
+            use_new_shuffle_tensor_slice=use_new_shuffle_tensor_slice,
+        )
+    elif type == SamplerType.EPOCH:
+        logger.info("sampler: epoch")
+        if advance > 0:
+            raise NotImplementedError("sampler advance > 0 is not supported")
+        size = size if size > 0 else sample_count
+        logger.info(f"# of samples / epoch: {size:,d}")
+        return EpochSampler(
+            size=size,
+            sample_count=sample_count,
+            shuffle=shuffle,
+            seed=seed,
+        )
+    elif type == SamplerType.DISTRIBUTED:
+        logger.info("sampler: distributed")
+        if size > 0:
+            raise ValueError("sampler size > 0 is invalid")
+        if advance > 0:
+            raise ValueError("sampler advance > 0 is invalid")
+        return torch.utils.data.DistributedSampler(
+            dataset=dataset,
+            shuffle=shuffle,
+            seed=seed,
+            drop_last=False,
+        )
+
+    logger.info("sampler: none")
+    return None
+
+
+T = TypeVar("T")
+
+
+def make_data_loader(
+    *,
+    dataset,
+    batch_size: int,
+    num_workers: int,
+    shuffle: bool = True,
+    seed: int = 0,
+    sampler_type: Optional[SamplerType] = SamplerType.INFINITE,
+    sampler_size: int = -1,
+    sampler_advance: int = 0,
+    drop_last: bool = True,
+    persistent_workers: bool = False,
+    collate_fn: Optional[Callable[[List[T]], Any]] = None,
+):
+    """
+    Creates a data loader with the specified parameters.
+
+    Args:
+        dataset: A dataset (third party, LaViDa or WebDataset).
+        batch_size: The size of batches to generate.
+        num_workers: The number of workers to use.
+        shuffle: Whether to shuffle samples.
+        seed: The random seed to use.
+        sampler_type: Which sampler to use: EPOCH, INFINITE, SHARDED_INFINITE, SHARDED_INFINITE_NEW, DISTRIBUTED or None.
+        sampler_size: The number of images per epoch (when applicable) or -1 for the entire dataset.
+        sampler_advance: How many samples to skip (when applicable).
+        drop_last: Whether the last non-full batch of data should be dropped.
+        persistent_workers: maintain the workers Dataset instances alive after a dataset has been consumed once.
+        collate_fn: Function that performs batch collation
+    """
+
+    sampler = _make_sampler(
+        dataset=dataset,
+        type=sampler_type,
+        shuffle=shuffle,
+        seed=seed,
+        size=sampler_size,
+        advance=sampler_advance,
+    )
+
+    logger.info("using PyTorch data loader")
+    data_loader = torch.utils.data.DataLoader(
+        dataset,
+        sampler=sampler,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=drop_last,
+        persistent_workers=persistent_workers,
+        collate_fn=collate_fn,
+    )
+
+    try:
+        logger.info(f"# of batches: {len(data_loader):,d}")
+    except TypeError:  # data loader has no length
+        logger.info("infinite data loader")
+    return data_loader
diff --git a/src/dinov2/data/masking.py b/src/dinov2/data/masking.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab12aa7bf138b916b16a9a2ed1a628a2759dbec6
--- /dev/null
+++ b/src/dinov2/data/masking.py
@@ -0,0 +1,86 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import random
+import math
+import numpy as np
+
+
+class MaskingGenerator:
+    def __init__(
+        self,
+        input_size,
+        num_masking_patches=None,
+        min_num_patches=4,
+        max_num_patches=None,
+        min_aspect=0.3,
+        max_aspect=None,
+    ):
+        if not isinstance(input_size, tuple):
+            input_size = (input_size,) * 2
+        self.height, self.width = input_size
+
+        self.num_patches = self.height * self.width
+        self.num_masking_patches = num_masking_patches
+
+        self.min_num_patches = min_num_patches
+        self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches
+
+        max_aspect = max_aspect or 1 / min_aspect
+        self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
+
+    def __repr__(self):
+        repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
+            self.height,
+            self.width,
+            self.min_num_patches,
+            self.max_num_patches,
+            self.num_masking_patches,
+            self.log_aspect_ratio[0],
+            self.log_aspect_ratio[1],
+        )
+        return repr_str
+
+    def get_shape(self):
+        return self.height, self.width
+
+    def _mask(self, mask, max_mask_patches):
+        delta = 0
+        for _ in range(10):
+            target_area = random.uniform(self.min_num_patches, max_mask_patches)
+            aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
+            h = int(round(math.sqrt(target_area * aspect_ratio)))
+            w = int(round(math.sqrt(target_area / aspect_ratio)))
+            if w < self.width and h < self.height:
+                top = random.randint(0, self.height - h)
+                left = random.randint(0, self.width - w)
+
+                num_masked = mask[top : top + h, left : left + w].sum()
+                # Overlap
+                if 0 < h * w - num_masked <= max_mask_patches:
+                    for i in range(top, top + h):
+                        for j in range(left, left + w):
+                            if mask[i, j] == 0:
+                                mask[i, j] = 1
+                                delta += 1
+
+                if delta > 0:
+                    break
+        return delta
+
+    def __call__(self, num_masking_patches=0):
+        mask = np.zeros(shape=self.get_shape(), dtype=bool)
+        mask_count = 0
+        while mask_count < num_masking_patches:
+            max_mask_patches = num_masking_patches - mask_count
+            max_mask_patches = min(max_mask_patches, self.max_num_patches)
+
+            delta = self._mask(mask, max_mask_patches)
+            if delta == 0:
+                break
+            else:
+                mask_count += delta
+
+        return mask
diff --git a/src/dinov2/data/samplers.py b/src/dinov2/data/samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..6562197d94652bb9a75a5fc722fcb2c65ca161be
--- /dev/null
+++ b/src/dinov2/data/samplers.py
@@ -0,0 +1,229 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import itertools
+from typing import Any, Optional
+import warnings
+
+import numpy as np
+import torch
+from torch.utils.data.sampler import Sampler
+
+import dinov2.distributed as distributed
+
+
+class EpochSampler(Sampler):
+    def __init__(
+        self,
+        *,
+        size: int,
+        sample_count: int,
+        shuffle: bool = False,
+        seed: int = 0,
+        start: Optional[int] = None,
+        step: Optional[int] = None,
+    ):
+        self._size = size
+        self._sample_count = sample_count
+        self._shuffle = shuffle
+        self._seed = seed
+        self._start = distributed.get_global_rank() if start is None else start
+        self._step = distributed.get_global_size() if step is None else step
+        self._epoch = 0
+
+    def __iter__(self):
+        count = (self._size + self._sample_count - 1) // self._sample_count
+        tiled_indices = np.tile(np.arange(self._sample_count), count)
+        if self._shuffle:
+            seed = self._seed * self._epoch if self._seed != 0 else self._epoch
+            rng = np.random.default_rng(seed)
+            iterable = rng.choice(tiled_indices, self._size, replace=False)
+        else:
+            iterable = tiled_indices[: self._size]
+
+        yield from itertools.islice(iterable, self._start, None, self._step)
+
+    def __len__(self):
+        return (self._size - self._start + self._step - 1) // self._step
+
+    def set_epoch(self, epoch):
+        self._epoch = epoch
+
+
+def _get_numpy_dtype(size: int) -> Any:
+    return np.int32 if size <= 2**31 else np.int64
+
+
+def _get_torch_dtype(size: int) -> Any:
+    return torch.int32 if size <= 2**31 else torch.int64
+
+
+def _generate_randperm_indices(*, size: int, generator: torch.Generator):
+    """Generate the indices of a random permutation."""
+    dtype = _get_torch_dtype(size)
+    # This is actually matching PyTorch's CPU implementation, see: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#L900-L921
+    perm = torch.arange(size, dtype=dtype)
+    for i in range(size):
+        j = torch.randint(i, size, size=(1,), generator=generator).item()
+
+        # Always swap even if no-op
+        value = perm[j].item()
+        perm[j] = perm[i].item()
+        perm[i] = value
+        yield value
+
+
+class InfiniteSampler(Sampler):
+    def __init__(
+        self,
+        *,
+        sample_count: int,
+        shuffle: bool = False,
+        seed: int = 0,
+        start: Optional[int] = None,
+        step: Optional[int] = None,
+        advance: int = 0,
+    ):
+        self._sample_count = sample_count
+        self._seed = seed
+        self._shuffle = shuffle
+        self._start = distributed.get_global_rank() if start is None else start
+        self._step = distributed.get_global_size() if step is None else step
+        self._advance = advance
+
+    def __iter__(self):
+        if self._shuffle:
+            iterator = self._shuffled_iterator()
+        else:
+            iterator = self._iterator()
+
+        yield from itertools.islice(iterator, self._advance, None)
+
+    def _iterator(self):
+        assert not self._shuffle
+
+        while True:
+            iterable = range(self._sample_count)
+            yield from itertools.islice(iterable, self._start, None, self._step)
+
+    def _shuffled_iterator(self):
+        assert self._shuffle
+
+        # Instantiate a generator here (rather than in the ctor) to keep the class
+        # picklable (requirement of mp.spawn)
+        generator = torch.Generator().manual_seed(self._seed)
+
+        while True:
+            iterable = _generate_randperm_indices(size=self._sample_count, generator=generator)
+            yield from itertools.islice(iterable, self._start, None, self._step)
+
+
+# The following function is somewhat equivalent to _new_shuffle_tensor_slice below,
+# but avoids a full in-place random permutation generation.
+def _shuffle_tensor_slice(
+    *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
+) -> np.ndarray:
+    stop = len(tensor)
+    count = stop // step
+    drop_count = stop - step * count
+    if drop_count:
+        warnings.warn(f"# of dropped samples: {drop_count}")
+
+    dtype = _get_numpy_dtype(stop)
+    result = np.empty(count, dtype=dtype)
+
+    for i in range(count):
+        j = torch.randint(0, i + 1, size=(1,), generator=generator).item() if i > 0 else 0
+
+        result[i] = result[j]
+        result[j] = tensor[start + i * step].item()
+
+    return result
+
+
+def _new_shuffle_tensor_slice(
+    *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
+) -> np.ndarray:
+    stop = len(tensor)
+    count = stop // step
+    dtype = torch.int64  # Needed for using randperm result as indices
+    count = stop // step
+    drop_count = stop - step * count
+    if drop_count:
+        warnings.warn(f"# of dropped samples: {drop_count}")
+    indices = torch.randperm(count, dtype=dtype, generator=generator)
+    return tensor[start::step][indices].numpy()
+
+
+def _make_seed(seed: int, start: int, iter_count: int) -> int:
+    # NOTE: Tried a few variants (including iter_count << 32), this one worked best.
+    return seed + start + (iter_count << 24)
+
+
+class ShardedInfiniteSampler(Sampler):
+    def __init__(
+        self,
+        *,
+        sample_count: int,
+        shuffle: bool = False,
+        seed: int = 0,
+        start: Optional[int] = None,
+        step: Optional[int] = None,
+        advance: int = 0,
+        use_new_shuffle_tensor_slice: bool = False,
+    ):
+        self._sample_count = sample_count
+        self._seed = seed
+        self._shuffle = shuffle
+        self._start = distributed.get_global_rank() if start is None else start
+        self._step = distributed.get_global_size() if step is None else step
+        self._advance = advance
+        self._iter_count = 0
+        self._shuffle_tensor_slice_fn = (
+            _new_shuffle_tensor_slice if use_new_shuffle_tensor_slice else _shuffle_tensor_slice
+        )
+
+    def __iter__(self):
+        iter_count = self._advance // self._sample_count
+        if iter_count > 0:
+            self._advance -= iter_count * self._sample_count
+            self._iter_count += iter_count
+
+        if self._shuffle:
+            iterator = self._shuffled_iterator()
+        else:
+            iterator = self._iterator()
+
+        yield from itertools.islice(iterator, self._advance, None)
+
+    def _iterator(self):
+        assert not self._shuffle
+
+        while True:
+            iterable = range(self._sample_count)
+            yield from itertools.islice(iterable, self._start, None, self._step)
+
+    def _shuffled_iterator(self):
+        assert self._shuffle
+
+        # Instantiate a generator here (rather than in the ctor) to be keep the class
+        # picklable (requirement of mp.spawn)
+        generator = torch.Generator()
+
+        # Always shuffle everything first
+        generator.manual_seed(self._seed)
+        dtype = _get_torch_dtype(self._sample_count)
+        perm = torch.randperm(self._sample_count, dtype=dtype, generator=generator)
+
+        while True:
+            # Re-seed on each iteration to allow skipping whole permutations
+            seed = _make_seed(self._seed, self._start, self._iter_count)
+            generator.manual_seed(seed)
+
+            iterable = self._shuffle_tensor_slice_fn(
+                tensor=perm, start=self._start, step=self._step, generator=generator
+            )
+            yield from iterable
+            self._iter_count += 1
diff --git a/src/dinov2/data/transforms.py b/src/dinov2/data/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb5f252b50c54d58f160528c9f2b00fad47103c7
--- /dev/null
+++ b/src/dinov2/data/transforms.py
@@ -0,0 +1,91 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from typing import Sequence
+
+import torch
+from torchvision import transforms
+
+
+class GaussianBlur(transforms.RandomApply):
+    """
+    Apply Gaussian Blur to the PIL image.
+    """
+
+    def __init__(self, *, p: float = 0.5, radius_min: float = 0.1, radius_max: float = 2.0):
+        # NOTE: torchvision is applying 1 - probability to return the original image
+        keep_p = 1 - p
+        transform = transforms.GaussianBlur(kernel_size=9, sigma=(radius_min, radius_max))
+        super().__init__(transforms=[transform], p=keep_p)
+
+
+class MaybeToTensor(transforms.ToTensor):
+    """
+    Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor, or keep as is if already a tensor.
+    """
+
+    def __call__(self, pic):
+        """
+        Args:
+            pic (PIL Image, numpy.ndarray or torch.tensor): Image to be converted to tensor.
+        Returns:
+            Tensor: Converted image.
+        """
+        if isinstance(pic, torch.Tensor):
+            return pic
+        return super().__call__(pic)
+
+
+# Use timm's names
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+
+def make_normalize_transform(
+    mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
+    std: Sequence[float] = IMAGENET_DEFAULT_STD,
+) -> transforms.Normalize:
+    return transforms.Normalize(mean=mean, std=std)
+
+
+# This roughly matches torchvision's preset for classification training:
+#   https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L6-L44
+def make_classification_train_transform(
+    *,
+    crop_size: int = 224,
+    interpolation=transforms.InterpolationMode.BICUBIC,
+    hflip_prob: float = 0.5,
+    mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
+    std: Sequence[float] = IMAGENET_DEFAULT_STD,
+):
+    transforms_list = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)]
+    if hflip_prob > 0.0:
+        transforms_list.append(transforms.RandomHorizontalFlip(hflip_prob))
+    transforms_list.extend(
+        [
+            MaybeToTensor(),
+            make_normalize_transform(mean=mean, std=std),
+        ]
+    )
+    return transforms.Compose(transforms_list)
+
+
+# This matches (roughly) torchvision's preset for classification evaluation:
+#   https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L47-L69
+def make_classification_eval_transform(
+    *,
+    resize_size: int = 256,
+    interpolation=transforms.InterpolationMode.BICUBIC,
+    crop_size: int = 224,
+    mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
+    std: Sequence[float] = IMAGENET_DEFAULT_STD,
+) -> transforms.Compose:
+    transforms_list = [
+        transforms.Resize(resize_size, interpolation=interpolation),
+        transforms.CenterCrop(crop_size),
+        MaybeToTensor(),
+        make_normalize_transform(mean=mean, std=std),
+    ]
+    return transforms.Compose(transforms_list)
diff --git a/src/dinov2/distributed/__init__.py b/src/dinov2/distributed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..23226f4536bf5acf4ffac242e9903d92863b246d
--- /dev/null
+++ b/src/dinov2/distributed/__init__.py
@@ -0,0 +1,270 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import os
+import random
+import re
+import socket
+from typing import Dict, List
+
+import torch
+import torch.distributed as dist
+
+_LOCAL_RANK = -1
+_LOCAL_WORLD_SIZE = -1
+
+
+def is_enabled() -> bool:
+    """
+    Returns:
+        True if distributed training is enabled
+    """
+    return dist.is_available() and dist.is_initialized()
+
+
+def get_global_size() -> int:
+    """
+    Returns:
+        The number of processes in the process group
+    """
+    return dist.get_world_size() if is_enabled() else 1
+
+
+def get_global_rank() -> int:
+    """
+    Returns:
+        The rank of the current process within the global process group.
+    """
+    return dist.get_rank() if is_enabled() else 0
+
+
+def get_local_rank() -> int:
+    """
+    Returns:
+        The rank of the current process within the local (per-machine) process group.
+    """
+    if not is_enabled():
+        return 0
+    assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE
+    return _LOCAL_RANK
+
+
+def get_local_size() -> int:
+    """
+    Returns:
+        The size of the per-machine process group,
+        i.e. the number of processes per machine.
+    """
+    if not is_enabled():
+        return 1
+    assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE
+    return _LOCAL_WORLD_SIZE
+
+
+def is_main_process() -> bool:
+    """
+    Returns:
+        True if the current process is the main one.
+    """
+    return get_global_rank() == 0
+
+
+def _restrict_print_to_main_process() -> None:
+    """
+    This function disables printing when not in the main process
+    """
+    import builtins as __builtin__
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_main_process() or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def _get_master_port(seed: int = 0) -> int:
+    MIN_MASTER_PORT, MAX_MASTER_PORT = (20_000, 60_000)
+
+    master_port_str = os.environ.get("MASTER_PORT")
+    if master_port_str is None:
+        rng = random.Random(seed)
+        return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT)
+
+    return int(master_port_str)
+
+
+def _get_available_port() -> int:
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        # A "" host address means INADDR_ANY i.e. binding to all interfaces.
+        # Note this is not compatible with IPv6.
+        s.bind(("", 0))
+        port = s.getsockname()[1]
+        return port
+
+
+_TORCH_DISTRIBUTED_ENV_VARS = (
+    "MASTER_ADDR",
+    "MASTER_PORT",
+    "RANK",
+    "WORLD_SIZE",
+    "LOCAL_RANK",
+    "LOCAL_WORLD_SIZE",
+)
+
+
+def _collect_env_vars() -> Dict[str, str]:
+    return {env_var: os.environ[env_var] for env_var in _TORCH_DISTRIBUTED_ENV_VARS if env_var in os.environ}
+
+
+def _is_slurm_job_process() -> bool:
+    return "SLURM_JOB_ID" in os.environ
+
+
+def _parse_slurm_node_list(s: str) -> List[str]:
+    nodes = []
+    # Extract "hostname", "hostname[1-2,3,4-5]," substrings
+    p = re.compile(r"(([^\[]+)(?:\[([^\]]+)\])?),?")
+    for m in p.finditer(s):
+        prefix, suffixes = s[m.start(2) : m.end(2)], s[m.start(3) : m.end(3)]
+        for suffix in suffixes.split(","):
+            span = suffix.split("-")
+            if len(span) == 1:
+                nodes.append(prefix + suffix)
+            else:
+                width = len(span[0])
+                start, end = int(span[0]), int(span[1]) + 1
+                nodes.extend([prefix + f"{i:0{width}}" for i in range(start, end)])
+    return nodes
+
+
+def _check_env_variable(key: str, new_value: str):
+    # Only check for difference with preset environment variables
+    if key in os.environ and os.environ[key] != new_value:
+        raise RuntimeError(f"Cannot export environment variables as {key} is already set")
+
+
+class _TorchDistributedEnvironment:
+    def __init__(self):
+        self.master_addr = "127.0.0.1"
+        self.master_port = 0
+        self.rank = -1
+        self.world_size = -1
+        self.local_rank = -1
+        self.local_world_size = -1
+
+        if _is_slurm_job_process():
+            return self._set_from_slurm_env()
+
+        env_vars = _collect_env_vars()
+        if not env_vars:
+            # Environment is not set
+            pass
+        elif len(env_vars) == len(_TORCH_DISTRIBUTED_ENV_VARS):
+            # Environment is fully set
+            return self._set_from_preset_env()
+        else:
+            # Environment is partially set
+            collected_env_vars = ", ".join(env_vars.keys())
+            raise RuntimeError(f"Partially set environment: {collected_env_vars}")
+
+        if torch.cuda.device_count() > 0:
+            return self._set_from_local()
+
+        raise RuntimeError("Can't initialize PyTorch distributed environment")
+
+    # Slurm job created with sbatch, submitit, etc...
+    def _set_from_slurm_env(self):
+        # logger.info("Initialization from Slurm environment")
+        job_id = int(os.environ["SLURM_JOB_ID"])
+        node_count = int(os.environ["SLURM_JOB_NUM_NODES"])
+        nodes = _parse_slurm_node_list(os.environ["SLURM_JOB_NODELIST"])
+        assert len(nodes) == node_count
+
+        self.master_addr = nodes[0]
+        self.master_port = _get_master_port(seed=job_id)
+        self.rank = int(os.environ["SLURM_PROCID"])
+        self.world_size = int(os.environ["SLURM_NTASKS"])
+        assert self.rank < self.world_size
+        self.local_rank = int(os.environ["SLURM_LOCALID"])
+        self.local_world_size = self.world_size // node_count
+        assert self.local_rank < self.local_world_size
+
+    # Single node job with preset environment (i.e. torchrun)
+    def _set_from_preset_env(self):
+        # logger.info("Initialization from preset environment")
+        self.master_addr = os.environ["MASTER_ADDR"]
+        self.master_port = os.environ["MASTER_PORT"]
+        self.rank = int(os.environ["RANK"])
+        self.world_size = int(os.environ["WORLD_SIZE"])
+        assert self.rank < self.world_size
+        self.local_rank = int(os.environ["LOCAL_RANK"])
+        self.local_world_size = int(os.environ["LOCAL_WORLD_SIZE"])
+        assert self.local_rank < self.local_world_size
+
+    # Single node and GPU job (i.e. local script run)
+    def _set_from_local(self):
+        # logger.info("Initialization from local")
+        self.master_addr = "127.0.0.1"
+        self.master_port = _get_available_port()
+        self.rank = 0
+        self.world_size = 1
+        self.local_rank = 0
+        self.local_world_size = 1
+
+    def export(self, *, overwrite: bool) -> "_TorchDistributedEnvironment":
+        # See the "Environment variable initialization" section from
+        # https://pytorch.org/docs/stable/distributed.html for the complete list of
+        # environment variables required for the env:// initialization method.
+        env_vars = {
+            "MASTER_ADDR": self.master_addr,
+            "MASTER_PORT": str(self.master_port),
+            "RANK": str(self.rank),
+            "WORLD_SIZE": str(self.world_size),
+            "LOCAL_RANK": str(self.local_rank),
+            "LOCAL_WORLD_SIZE": str(self.local_world_size),
+        }
+        if not overwrite:
+            for k, v in env_vars.items():
+                _check_env_variable(k, v)
+
+        os.environ.update(env_vars)
+        return self
+
+
+def enable(*, set_cuda_current_device: bool = True, overwrite: bool = False, allow_nccl_timeout: bool = False):
+    """Enable distributed mode
+
+    Args:
+        set_cuda_current_device: If True, call torch.cuda.set_device() to set the
+            current PyTorch CUDA device to the one matching the local rank.
+        overwrite: If True, overwrites already set variables. Else fails.
+    """
+
+    global _LOCAL_RANK, _LOCAL_WORLD_SIZE
+    if _LOCAL_RANK >= 0 or _LOCAL_WORLD_SIZE >= 0:
+        raise RuntimeError("Distributed mode has already been enabled")
+    torch_env = _TorchDistributedEnvironment()
+    torch_env.export(overwrite=overwrite)
+
+    if set_cuda_current_device:
+        torch.cuda.set_device(torch_env.local_rank)
+
+    if allow_nccl_timeout:
+        # This allows to use torch distributed timeout in a NCCL backend
+        key, value = "NCCL_ASYNC_ERROR_HANDLING", "1"
+        if not overwrite:
+            _check_env_variable(key, value)
+        os.environ[key] = value
+
+    dist.init_process_group(backend="nccl")
+    dist.barrier()
+
+    # Finalize setup
+    _LOCAL_RANK = torch_env.local_rank
+    _LOCAL_WORLD_SIZE = torch_env.local_world_size
+    _restrict_print_to_main_process()
diff --git a/src/dinov2/eval/__init__.py b/src/dinov2/eval/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9
--- /dev/null
+++ b/src/dinov2/eval/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/src/dinov2/eval/depth/__init__.py b/src/dinov2/eval/depth/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9
--- /dev/null
+++ b/src/dinov2/eval/depth/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/src/dinov2/eval/depth/models/__init__.py b/src/dinov2/eval/depth/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a5825181dc2189424b5c58d245b36919cbc5b2e
--- /dev/null
+++ b/src/dinov2/eval/depth/models/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .backbones import *  # noqa: F403
+from .builder import BACKBONES, DEPTHER, HEADS, LOSSES, build_backbone, build_depther, build_head, build_loss
+from .decode_heads import *  # noqa: F403
+from .depther import *  # noqa: F403
+from .losses import *  # noqa: F403
diff --git a/src/dinov2/eval/depth/models/backbones/__init__.py b/src/dinov2/eval/depth/models/backbones/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..520d75bc6e064b9d64487293604ac1bda6e2b6f7
--- /dev/null
+++ b/src/dinov2/eval/depth/models/backbones/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .vision_transformer import DinoVisionTransformer
diff --git a/src/dinov2/eval/depth/models/backbones/vision_transformer.py b/src/dinov2/eval/depth/models/backbones/vision_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..69bda46fd69eb7dabb8f5b60e6fa459fdc21aeab
--- /dev/null
+++ b/src/dinov2/eval/depth/models/backbones/vision_transformer.py
@@ -0,0 +1,16 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from mmcv.runner import BaseModule
+
+from ..builder import BACKBONES
+
+
+@BACKBONES.register_module()
+class DinoVisionTransformer(BaseModule):
+    """Vision Transformer."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__()
diff --git a/src/dinov2/eval/depth/models/builder.py b/src/dinov2/eval/depth/models/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c152643435308afcff60b07cd68ea979fe1d90cb
--- /dev/null
+++ b/src/dinov2/eval/depth/models/builder.py
@@ -0,0 +1,49 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import warnings
+
+from mmcv.cnn import MODELS as MMCV_MODELS
+from mmcv.cnn.bricks.registry import ATTENTION as MMCV_ATTENTION
+from mmcv.utils import Registry
+
+MODELS = Registry("models", parent=MMCV_MODELS)
+ATTENTION = Registry("attention", parent=MMCV_ATTENTION)
+
+
+BACKBONES = MODELS
+NECKS = MODELS
+HEADS = MODELS
+LOSSES = MODELS
+DEPTHER = MODELS
+
+
+def build_backbone(cfg):
+    """Build backbone."""
+    return BACKBONES.build(cfg)
+
+
+def build_neck(cfg):
+    """Build neck."""
+    return NECKS.build(cfg)
+
+
+def build_head(cfg):
+    """Build head."""
+    return HEADS.build(cfg)
+
+
+def build_loss(cfg):
+    """Build loss."""
+    return LOSSES.build(cfg)
+
+
+def build_depther(cfg, train_cfg=None, test_cfg=None):
+    """Build depther."""
+    if train_cfg is not None or test_cfg is not None:
+        warnings.warn("train_cfg and test_cfg is deprecated, " "please specify them in model", UserWarning)
+    assert cfg.get("train_cfg") is None or train_cfg is None, "train_cfg specified in both outer field and model field "
+    assert cfg.get("test_cfg") is None or test_cfg is None, "test_cfg specified in both outer field and model field "
+    return DEPTHER.build(cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
diff --git a/src/dinov2/eval/depth/models/decode_heads/__init__.py b/src/dinov2/eval/depth/models/decode_heads/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd0f0754a5b01d7622c1f26bf3f60daea19da4e8
--- /dev/null
+++ b/src/dinov2/eval/depth/models/decode_heads/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .dpt_head import DPTHead
+from .linear_head import BNHead
diff --git a/src/dinov2/eval/depth/models/decode_heads/decode_head.py b/src/dinov2/eval/depth/models/decode_heads/decode_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8c867a3ec687090b280d90bb86aee435320acda
--- /dev/null
+++ b/src/dinov2/eval/depth/models/decode_heads/decode_head.py
@@ -0,0 +1,225 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import copy
+from abc import ABCMeta, abstractmethod
+
+import mmcv
+import numpy as np
+import torch
+import torch.nn as nn
+from mmcv.runner import BaseModule, auto_fp16, force_fp32
+
+from ...ops import resize
+from ..builder import build_loss
+
+
+class DepthBaseDecodeHead(BaseModule, metaclass=ABCMeta):
+    """Base class for BaseDecodeHead.
+
+    Args:
+        in_channels (List): Input channels.
+        channels (int): Channels after modules, before conv_depth.
+        conv_cfg (dict|None): Config of conv layers. Default: None.
+        act_cfg (dict): Config of activation layers.
+            Default: dict(type='ReLU')
+        loss_decode (dict): Config of decode loss.
+            Default: dict(type='SigLoss').
+        sampler (dict|None): The config of depth map sampler.
+            Default: None.
+        align_corners (bool): align_corners argument of F.interpolate.
+            Default: False.
+        min_depth (int): Min depth in dataset setting.
+            Default: 1e-3.
+        max_depth (int): Max depth in dataset setting.
+            Default: None.
+        norm_cfg (dict|None): Config of norm layers.
+            Default: None.
+        classify (bool): Whether predict depth in a cls.-reg. manner.
+            Default: False.
+        n_bins (int): The number of bins used in cls. step.
+            Default: 256.
+        bins_strategy (str): The discrete strategy used in cls. step.
+            Default: 'UD'.
+        norm_strategy (str): The norm strategy on cls. probability
+            distribution. Default: 'linear'
+        scale_up (str): Whether predict depth in a scale-up manner.
+            Default: False.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        channels=96,
+        conv_cfg=None,
+        act_cfg=dict(type="ReLU"),
+        loss_decode=dict(type="SigLoss", valid_mask=True, loss_weight=10),
+        sampler=None,
+        align_corners=False,
+        min_depth=1e-3,
+        max_depth=None,
+        norm_cfg=None,
+        classify=False,
+        n_bins=256,
+        bins_strategy="UD",
+        norm_strategy="linear",
+        scale_up=False,
+    ):
+        super(DepthBaseDecodeHead, self).__init__()
+
+        self.in_channels = in_channels
+        self.channels = channels
+        self.conv_cfg = conv_cfg
+        self.act_cfg = act_cfg
+        if isinstance(loss_decode, dict):
+            self.loss_decode = build_loss(loss_decode)
+        elif isinstance(loss_decode, (list, tuple)):
+            self.loss_decode = nn.ModuleList()
+            for loss in loss_decode:
+                self.loss_decode.append(build_loss(loss))
+        self.align_corners = align_corners
+        self.min_depth = min_depth
+        self.max_depth = max_depth
+        self.norm_cfg = norm_cfg
+        self.classify = classify
+        self.n_bins = n_bins
+        self.scale_up = scale_up
+
+        if self.classify:
+            assert bins_strategy in ["UD", "SID"], "Support bins_strategy: UD, SID"
+            assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid"
+
+            self.bins_strategy = bins_strategy
+            self.norm_strategy = norm_strategy
+            self.softmax = nn.Softmax(dim=1)
+            self.conv_depth = nn.Conv2d(channels, n_bins, kernel_size=3, padding=1, stride=1)
+        else:
+            self.conv_depth = nn.Conv2d(channels, 1, kernel_size=3, padding=1, stride=1)
+
+        self.fp16_enabled = False
+        self.relu = nn.ReLU()
+        self.sigmoid = nn.Sigmoid()
+
+    def extra_repr(self):
+        """Extra repr."""
+        s = f"align_corners={self.align_corners}"
+        return s
+
+    @auto_fp16()
+    @abstractmethod
+    def forward(self, inputs, img_metas):
+        """Placeholder of forward function."""
+        pass
+
+    def forward_train(self, img, inputs, img_metas, depth_gt, train_cfg):
+        """Forward function for training.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            depth_gt (Tensor): GT depth
+            train_cfg (dict): The training config.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        depth_pred = self.forward(inputs, img_metas)
+        losses = self.losses(depth_pred, depth_gt)
+
+        log_imgs = self.log_images(img[0], depth_pred[0], depth_gt[0], img_metas[0])
+        losses.update(**log_imgs)
+
+        return losses
+
+    def forward_test(self, inputs, img_metas, test_cfg):
+        """Forward function for testing.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            test_cfg (dict): The testing config.
+
+        Returns:
+            Tensor: Output depth map.
+        """
+        return self.forward(inputs, img_metas)
+
+    def depth_pred(self, feat):
+        """Prediction each pixel."""
+        if self.classify:
+            logit = self.conv_depth(feat)
+
+            if self.bins_strategy == "UD":
+                bins = torch.linspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
+            elif self.bins_strategy == "SID":
+                bins = torch.logspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
+
+            # following Adabins, default linear
+            if self.norm_strategy == "linear":
+                logit = torch.relu(logit)
+                eps = 0.1
+                logit = logit + eps
+                logit = logit / logit.sum(dim=1, keepdim=True)
+            elif self.norm_strategy == "softmax":
+                logit = torch.softmax(logit, dim=1)
+            elif self.norm_strategy == "sigmoid":
+                logit = torch.sigmoid(logit)
+                logit = logit / logit.sum(dim=1, keepdim=True)
+
+            output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1)
+
+        else:
+            if self.scale_up:
+                output = self.sigmoid(self.conv_depth(feat)) * self.max_depth
+            else:
+                output = self.relu(self.conv_depth(feat)) + self.min_depth
+        return output
+
+    @force_fp32(apply_to=("depth_pred",))
+    def losses(self, depth_pred, depth_gt):
+        """Compute depth loss."""
+        loss = dict()
+        depth_pred = resize(
+            input=depth_pred, size=depth_gt.shape[2:], mode="bilinear", align_corners=self.align_corners, warning=False
+        )
+        if not isinstance(self.loss_decode, nn.ModuleList):
+            losses_decode = [self.loss_decode]
+        else:
+            losses_decode = self.loss_decode
+        for loss_decode in losses_decode:
+            if loss_decode.loss_name not in loss:
+                loss[loss_decode.loss_name] = loss_decode(depth_pred, depth_gt)
+            else:
+                loss[loss_decode.loss_name] += loss_decode(depth_pred, depth_gt)
+        return loss
+
+    def log_images(self, img_path, depth_pred, depth_gt, img_meta):
+        show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0))
+        show_img = show_img.numpy().astype(np.float32)
+        show_img = mmcv.imdenormalize(
+            show_img,
+            img_meta["img_norm_cfg"]["mean"],
+            img_meta["img_norm_cfg"]["std"],
+            img_meta["img_norm_cfg"]["to_rgb"],
+        )
+        show_img = np.clip(show_img, 0, 255)
+        show_img = show_img.astype(np.uint8)
+        show_img = show_img[:, :, ::-1]
+        show_img = show_img.transpose(0, 2, 1)
+        show_img = show_img.transpose(1, 0, 2)
+
+        depth_pred = depth_pred / torch.max(depth_pred)
+        depth_gt = depth_gt / torch.max(depth_gt)
+
+        depth_pred_color = copy.deepcopy(depth_pred.detach().cpu())
+        depth_gt_color = copy.deepcopy(depth_gt.detach().cpu())
+
+        return {"img_rgb": show_img, "img_depth_pred": depth_pred_color, "img_depth_gt": depth_gt_color}
diff --git a/src/dinov2/eval/depth/models/decode_heads/dpt_head.py b/src/dinov2/eval/depth/models/decode_heads/dpt_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6c6d9470d78e1d944cc505f97865f026a9458d3
--- /dev/null
+++ b/src/dinov2/eval/depth/models/decode_heads/dpt_head.py
@@ -0,0 +1,270 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, Linear, build_activation_layer
+from mmcv.runner import BaseModule
+
+from ...ops import resize
+from ..builder import HEADS
+from .decode_head import DepthBaseDecodeHead
+
+
+class Interpolate(nn.Module):
+    def __init__(self, scale_factor, mode, align_corners=False):
+        super(Interpolate, self).__init__()
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
+        return x
+
+
+class HeadDepth(nn.Module):
+    def __init__(self, features):
+        super(HeadDepth, self).__init__()
+        self.head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+        )
+
+    def forward(self, x):
+        x = self.head(x)
+        return x
+
+
+class ReassembleBlocks(BaseModule):
+    """ViTPostProcessBlock, process cls_token in ViT backbone output and
+    rearrange the feature vector to feature map.
+    Args:
+        in_channels (int): ViT feature channels. Default: 768.
+        out_channels (List): output channels of each stage.
+            Default: [96, 192, 384, 768].
+        readout_type (str): Type of readout operation. Default: 'ignore'.
+        patch_size (int): The patch size. Default: 16.
+        init_cfg (dict, optional): Initialization config dict. Default: None.
+    """
+
+    def __init__(
+        self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16, init_cfg=None
+    ):
+        super(ReassembleBlocks, self).__init__(init_cfg)
+
+        assert readout_type in ["ignore", "add", "project"]
+        self.readout_type = readout_type
+        self.patch_size = patch_size
+
+        self.projects = nn.ModuleList(
+            [
+                ConvModule(
+                    in_channels=in_channels,
+                    out_channels=out_channel,
+                    kernel_size=1,
+                    act_cfg=None,
+                )
+                for out_channel in out_channels
+            ]
+        )
+
+        self.resize_layers = nn.ModuleList(
+            [
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0
+                ),
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0
+                ),
+                nn.Identity(),
+                nn.Conv2d(
+                    in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1
+                ),
+            ]
+        )
+        if self.readout_type == "project":
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(
+                    nn.Sequential(Linear(2 * in_channels, in_channels), build_activation_layer(dict(type="GELU")))
+                )
+
+    def forward(self, inputs):
+        assert isinstance(inputs, list)
+        out = []
+        for i, x in enumerate(inputs):
+            assert len(x) == 2
+            x, cls_token = x[0], x[1]
+            feature_shape = x.shape
+            if self.readout_type == "project":
+                x = x.flatten(2).permute((0, 2, 1))
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+                x = x.permute(0, 2, 1).reshape(feature_shape)
+            elif self.readout_type == "add":
+                x = x.flatten(2) + cls_token.unsqueeze(-1)
+                x = x.reshape(feature_shape)
+            else:
+                pass
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+            out.append(x)
+        return out
+
+
+class PreActResidualConvUnit(BaseModule):
+    """ResidualConvUnit, pre-activate residual unit.
+    Args:
+        in_channels (int): number of channels in the input feature map.
+        act_cfg (dict): dictionary to construct and config activation layer.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+        stride (int): stride of the first block. Default: 1
+        dilation (int): dilation rate for convs layers. Default: 1.
+        init_cfg (dict, optional): Initialization config dict. Default: None.
+    """
+
+    def __init__(self, in_channels, act_cfg, norm_cfg, stride=1, dilation=1, init_cfg=None):
+        super(PreActResidualConvUnit, self).__init__(init_cfg)
+
+        self.conv1 = ConvModule(
+            in_channels,
+            in_channels,
+            3,
+            stride=stride,
+            padding=dilation,
+            dilation=dilation,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg,
+            bias=False,
+            order=("act", "conv", "norm"),
+        )
+
+        self.conv2 = ConvModule(
+            in_channels,
+            in_channels,
+            3,
+            padding=1,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg,
+            bias=False,
+            order=("act", "conv", "norm"),
+        )
+
+    def forward(self, inputs):
+        inputs_ = inputs.clone()
+        x = self.conv1(inputs)
+        x = self.conv2(x)
+        return x + inputs_
+
+
+class FeatureFusionBlock(BaseModule):
+    """FeatureFusionBlock, merge feature map from different stages.
+    Args:
+        in_channels (int): Input channels.
+        act_cfg (dict): The activation config for ResidualConvUnit.
+        norm_cfg (dict): Config dict for normalization layer.
+        expand (bool): Whether expand the channels in post process block.
+            Default: False.
+        align_corners (bool): align_corner setting for bilinear upsample.
+            Default: True.
+        init_cfg (dict, optional): Initialization config dict. Default: None.
+    """
+
+    def __init__(self, in_channels, act_cfg, norm_cfg, expand=False, align_corners=True, init_cfg=None):
+        super(FeatureFusionBlock, self).__init__(init_cfg)
+
+        self.in_channels = in_channels
+        self.expand = expand
+        self.align_corners = align_corners
+
+        self.out_channels = in_channels
+        if self.expand:
+            self.out_channels = in_channels // 2
+
+        self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_cfg=None, bias=True)
+
+        self.res_conv_unit1 = PreActResidualConvUnit(in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg)
+        self.res_conv_unit2 = PreActResidualConvUnit(in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg)
+
+    def forward(self, *inputs):
+        x = inputs[0]
+        if len(inputs) == 2:
+            if x.shape != inputs[1].shape:
+                res = resize(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False)
+            else:
+                res = inputs[1]
+            x = x + self.res_conv_unit1(res)
+        x = self.res_conv_unit2(x)
+        x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners)
+        x = self.project(x)
+        return x
+
+
+@HEADS.register_module()
+class DPTHead(DepthBaseDecodeHead):
+    """Vision Transformers for Dense Prediction.
+    This head is implemented of `DPT <https://arxiv.org/abs/2103.13413>`_.
+    Args:
+        embed_dims (int): The embed dimension of the ViT backbone.
+            Default: 768.
+        post_process_channels (List): Out channels of post process conv
+            layers. Default: [96, 192, 384, 768].
+        readout_type (str): Type of readout operation. Default: 'ignore'.
+        patch_size (int): The patch size. Default: 16.
+        expand_channels (bool): Whether expand the channels in post process
+            block. Default: False.
+    """
+
+    def __init__(
+        self,
+        embed_dims=768,
+        post_process_channels=[96, 192, 384, 768],
+        readout_type="ignore",
+        patch_size=16,
+        expand_channels=False,
+        **kwargs
+    ):
+        super(DPTHead, self).__init__(**kwargs)
+
+        self.in_channels = self.in_channels
+        self.expand_channels = expand_channels
+        self.reassemble_blocks = ReassembleBlocks(embed_dims, post_process_channels, readout_type, patch_size)
+
+        self.post_process_channels = [
+            channel * math.pow(2, i) if expand_channels else channel for i, channel in enumerate(post_process_channels)
+        ]
+        self.convs = nn.ModuleList()
+        for channel in self.post_process_channels:
+            self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_cfg=None, bias=False))
+        self.fusion_blocks = nn.ModuleList()
+        for _ in range(len(self.convs)):
+            self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_cfg, self.norm_cfg))
+        self.fusion_blocks[0].res_conv_unit1 = None
+        self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_cfg=self.norm_cfg)
+        self.num_fusion_blocks = len(self.fusion_blocks)
+        self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers)
+        self.num_post_process_channels = len(self.post_process_channels)
+        assert self.num_fusion_blocks == self.num_reassemble_blocks
+        assert self.num_reassemble_blocks == self.num_post_process_channels
+        self.conv_depth = HeadDepth(self.channels)
+
+    def forward(self, inputs, img_metas):
+        assert len(inputs) == self.num_reassemble_blocks
+        x = [inp for inp in inputs]
+        x = self.reassemble_blocks(x)
+        x = [self.convs[i](feature) for i, feature in enumerate(x)]
+        out = self.fusion_blocks[0](x[-1])
+        for i in range(1, len(self.fusion_blocks)):
+            out = self.fusion_blocks[i](out, x[-(i + 1)])
+        out = self.project(out)
+        out = self.depth_pred(out)
+        return out
diff --git a/src/dinov2/eval/depth/models/decode_heads/linear_head.py b/src/dinov2/eval/depth/models/decode_heads/linear_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..3da1436f6a3f0bcc389d74ed86d44d455d2f7a87
--- /dev/null
+++ b/src/dinov2/eval/depth/models/decode_heads/linear_head.py
@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from ...ops import resize
+from ..builder import HEADS
+from .decode_head import DepthBaseDecodeHead
+
+
+@HEADS.register_module()
+class BNHead(DepthBaseDecodeHead):
+    """Just a batchnorm."""
+
+    def __init__(self, input_transform="resize_concat", in_index=(0, 1, 2, 3), upsample=1, **kwargs):
+        super().__init__(**kwargs)
+        self.input_transform = input_transform
+        self.in_index = in_index
+        self.upsample = upsample
+        # self.bn = nn.SyncBatchNorm(self.in_channels)
+        if self.classify:
+            self.conv_depth = nn.Conv2d(self.channels, self.n_bins, kernel_size=1, padding=0, stride=1)
+        else:
+            self.conv_depth = nn.Conv2d(self.channels, 1, kernel_size=1, padding=0, stride=1)
+
+    def _transform_inputs(self, inputs):
+        """Transform inputs for decoder.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            Tensor: The transformed inputs
+        """
+
+        if "concat" in self.input_transform:
+            inputs = [inputs[i] for i in self.in_index]
+            if "resize" in self.input_transform:
+                inputs = [
+                    resize(
+                        input=x,
+                        size=[s * self.upsample for s in inputs[0].shape[2:]],
+                        mode="bilinear",
+                        align_corners=self.align_corners,
+                    )
+                    for x in inputs
+                ]
+            inputs = torch.cat(inputs, dim=1)
+        elif self.input_transform == "multiple_select":
+            inputs = [inputs[i] for i in self.in_index]
+        else:
+            inputs = inputs[self.in_index]
+
+        return inputs
+
+    def _forward_feature(self, inputs, img_metas=None, **kwargs):
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
+        # accept lists (for cls token)
+        inputs = list(inputs)
+        for i, x in enumerate(inputs):
+            if len(x) == 2:
+                x, cls_token = x[0], x[1]
+                if len(x.shape) == 2:
+                    x = x[:, :, None, None]
+                cls_token = cls_token[:, :, None, None].expand_as(x)
+                inputs[i] = torch.cat((x, cls_token), 1)
+            else:
+                x = x[0]
+                if len(x.shape) == 2:
+                    x = x[:, :, None, None]
+                inputs[i] = x
+        x = self._transform_inputs(inputs)
+        # feats = self.bn(x)
+        return x
+
+    def forward(self, inputs, img_metas=None, **kwargs):
+        """Forward function."""
+        output = self._forward_feature(inputs, img_metas=img_metas, **kwargs)
+        output = self.depth_pred(output)
+
+        return output
diff --git a/src/dinov2/eval/depth/models/depther/__init__.py b/src/dinov2/eval/depth/models/depther/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..be99743bf6c773d05f2b74524116e368c0cfcba0
--- /dev/null
+++ b/src/dinov2/eval/depth/models/depther/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .base import BaseDepther
+from .encoder_decoder import DepthEncoderDecoder
diff --git a/src/dinov2/eval/depth/models/depther/base.py b/src/dinov2/eval/depth/models/depther/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..e133a825a888167f90d95d67803609d6cac7ff55
--- /dev/null
+++ b/src/dinov2/eval/depth/models/depther/base.py
@@ -0,0 +1,194 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from abc import ABCMeta, abstractmethod
+from collections import OrderedDict
+
+import torch
+import torch.distributed as dist
+from mmcv.runner import BaseModule, auto_fp16
+
+
+class BaseDepther(BaseModule, metaclass=ABCMeta):
+    """Base class for depther."""
+
+    def __init__(self, init_cfg=None):
+        super(BaseDepther, self).__init__(init_cfg)
+        self.fp16_enabled = False
+
+    @property
+    def with_neck(self):
+        """bool: whether the depther has neck"""
+        return hasattr(self, "neck") and self.neck is not None
+
+    @property
+    def with_auxiliary_head(self):
+        """bool: whether the depther has auxiliary head"""
+        return hasattr(self, "auxiliary_head") and self.auxiliary_head is not None
+
+    @property
+    def with_decode_head(self):
+        """bool: whether the depther has decode head"""
+        return hasattr(self, "decode_head") and self.decode_head is not None
+
+    @abstractmethod
+    def extract_feat(self, imgs):
+        """Placeholder for extract features from images."""
+        pass
+
+    @abstractmethod
+    def encode_decode(self, img, img_metas):
+        """Placeholder for encode images with backbone and decode into a
+        semantic depth map of the same size as input."""
+        pass
+
+    @abstractmethod
+    def forward_train(self, imgs, img_metas, **kwargs):
+        """Placeholder for Forward function for training."""
+        pass
+
+    @abstractmethod
+    def simple_test(self, img, img_meta, **kwargs):
+        """Placeholder for single image test."""
+        pass
+
+    @abstractmethod
+    def aug_test(self, imgs, img_metas, **kwargs):
+        """Placeholder for augmentation test."""
+        pass
+
+    def forward_test(self, imgs, img_metas, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (List[List[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch.
+        """
+        for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]:
+            if not isinstance(var, list):
+                raise TypeError(f"{name} must be a list, but got " f"{type(var)}")
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})")
+        # all images in the same aug batch all of the same ori_shape and pad
+        # shape
+        for img_meta in img_metas:
+            ori_shapes = [_["ori_shape"] for _ in img_meta]
+            assert all(shape == ori_shapes[0] for shape in ori_shapes)
+            img_shapes = [_["img_shape"] for _ in img_meta]
+            assert all(shape == img_shapes[0] for shape in img_shapes)
+            pad_shapes = [_["pad_shape"] for _ in img_meta]
+            assert all(shape == pad_shapes[0] for shape in pad_shapes)
+
+        if num_augs == 1:
+            return self.simple_test(imgs[0], img_metas[0], **kwargs)
+        else:
+            return self.aug_test(imgs, img_metas, **kwargs)
+
+    @auto_fp16(apply_to=("img",))
+    def forward(self, img, img_metas, return_loss=True, **kwargs):
+        """Calls either :func:`forward_train` or :func:`forward_test` depending
+        on whether ``return_loss`` is ``True``.
+
+        Note this setting will change the expected inputs. When
+        ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
+        and List[dict]), and when ``resturn_loss=False``, img and img_meta
+        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
+        the outer list indicating test time augmentations.
+        """
+        if return_loss:
+            return self.forward_train(img, img_metas, **kwargs)
+        else:
+            return self.forward_test(img, img_metas, **kwargs)
+
+    def train_step(self, data_batch, optimizer, **kwargs):
+        """The iteration step during training.
+
+        This method defines an iteration step during training, except for the
+        back propagation and optimizer updating, which are done in an optimizer
+        hook. Note that in some complicated cases or models, the whole process
+        including back propagation and optimizer updating is also defined in
+        this method, such as GAN.
+
+        Args:
+            data (dict): The output of dataloader.
+            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
+                runner is passed to ``train_step()``. This argument is unused
+                and reserved.
+
+        Returns:
+            dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
+                ``num_samples``.
+                ``loss`` is a tensor for back propagation, which can be a
+                weighted sum of multiple losses.
+                ``log_vars`` contains all the variables to be sent to the
+                logger.
+                ``num_samples`` indicates the batch size (when the model is
+                DDP, it means the batch size on each GPU), which is used for
+                averaging the logs.
+        """
+        losses = self(**data_batch)
+
+        # split losses and images
+        real_losses = {}
+        log_imgs = {}
+        for k, v in losses.items():
+            if "img" in k:
+                log_imgs[k] = v
+            else:
+                real_losses[k] = v
+
+        loss, log_vars = self._parse_losses(real_losses)
+
+        outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs)
+
+        return outputs
+
+    def val_step(self, data_batch, **kwargs):
+        """The iteration step during validation.
+
+        This method shares the same signature as :func:`train_step`, but used
+        during val epochs. Note that the evaluation after training epochs is
+        not implemented with this method, but an evaluation hook.
+        """
+        output = self(**data_batch, **kwargs)
+        return output
+
+    @staticmethod
+    def _parse_losses(losses):
+        """Parse the raw outputs (losses) of the network.
+
+        Args:
+            losses (dict): Raw output of the network, which usually contain
+                losses and other necessary information.
+
+        Returns:
+            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
+                which may be a weighted sum of all losses, log_vars contains
+                all the variables to be sent to the logger.
+        """
+        log_vars = OrderedDict()
+        for loss_name, loss_value in losses.items():
+            if isinstance(loss_value, torch.Tensor):
+                log_vars[loss_name] = loss_value.mean()
+            elif isinstance(loss_value, list):
+                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
+            else:
+                raise TypeError(f"{loss_name} is not a tensor or list of tensors")
+
+        loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key)
+
+        log_vars["loss"] = loss
+        for loss_name, loss_value in log_vars.items():
+            # reduce loss when distributed training
+            if dist.is_available() and dist.is_initialized():
+                loss_value = loss_value.data.clone()
+                dist.all_reduce(loss_value.div_(dist.get_world_size()))
+            log_vars[loss_name] = loss_value.item()
+
+        return loss, log_vars
diff --git a/src/dinov2/eval/depth/models/depther/encoder_decoder.py b/src/dinov2/eval/depth/models/depther/encoder_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b0ec2dd314fdf8ccf4414d81afb95326b7dc0c9
--- /dev/null
+++ b/src/dinov2/eval/depth/models/depther/encoder_decoder.py
@@ -0,0 +1,236 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn.functional as F
+
+from ...models import builder
+from ...models.builder import DEPTHER
+from ...ops import resize
+from .base import BaseDepther
+
+
+def add_prefix(inputs, prefix):
+    """Add prefix for dict.
+
+    Args:
+        inputs (dict): The input dict with str keys.
+        prefix (str): The prefix to add.
+
+    Returns:
+
+        dict: The dict with keys updated with ``prefix``.
+    """
+
+    outputs = dict()
+    for name, value in inputs.items():
+        outputs[f"{prefix}.{name}"] = value
+
+    return outputs
+
+
+@DEPTHER.register_module()
+class DepthEncoderDecoder(BaseDepther):
+    """Encoder Decoder depther.
+
+    EncoderDecoder typically consists of backbone, (neck) and decode_head.
+    """
+
+    def __init__(self, backbone, decode_head, neck=None, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
+        super(DepthEncoderDecoder, self).__init__(init_cfg)
+        if pretrained is not None:
+            assert backbone.get("pretrained") is None, "both backbone and depther set pretrained weight"
+            backbone.pretrained = pretrained
+        self.backbone = builder.build_backbone(backbone)
+        self._init_decode_head(decode_head)
+
+        if neck is not None:
+            self.neck = builder.build_neck(neck)
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+        assert self.with_decode_head
+
+    def _init_decode_head(self, decode_head):
+        """Initialize ``decode_head``"""
+        self.decode_head = builder.build_head(decode_head)
+        self.align_corners = self.decode_head.align_corners
+
+    def extract_feat(self, img):
+        """Extract features from images."""
+        x = self.backbone(img)
+        if self.with_neck:
+            x = self.neck(x)
+        return x
+
+    def encode_decode(self, img, img_metas, rescale=True, size=None):
+        """Encode images with backbone and decode into a depth estimation
+        map of the same size as input."""
+        x = self.extract_feat(img)
+        out = self._decode_head_forward_test(x, img_metas)
+        # crop the pred depth to the certain range.
+        out = torch.clamp(out, min=self.decode_head.min_depth, max=self.decode_head.max_depth)
+        if rescale:
+            if size is None:
+                if img_metas is not None:
+                    size = img_metas[0]["ori_shape"][:2]
+                else:
+                    size = img.shape[2:]
+            out = resize(input=out, size=size, mode="bilinear", align_corners=self.align_corners)
+        return out
+
+    def _decode_head_forward_train(self, img, x, img_metas, depth_gt, **kwargs):
+        """Run forward function and calculate loss for decode head in
+        training."""
+        losses = dict()
+        loss_decode = self.decode_head.forward_train(img, x, img_metas, depth_gt, self.train_cfg, **kwargs)
+        losses.update(add_prefix(loss_decode, "decode"))
+        return losses
+
+    def _decode_head_forward_test(self, x, img_metas):
+        """Run forward function and calculate loss for decode head in
+        inference."""
+        depth_pred = self.decode_head.forward_test(x, img_metas, self.test_cfg)
+        return depth_pred
+
+    def forward_dummy(self, img):
+        """Dummy forward function."""
+        depth = self.encode_decode(img, None)
+
+        return depth
+
+    def forward_train(self, img, img_metas, depth_gt, **kwargs):
+        """Forward function for training.
+
+        Args:
+            img (Tensor): Input images.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            depth_gt (Tensor): Depth gt
+                used if the architecture supports depth estimation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+
+        x = self.extract_feat(img)
+
+        losses = dict()
+
+        # the last of x saves the info from neck
+        loss_decode = self._decode_head_forward_train(img, x, img_metas, depth_gt, **kwargs)
+
+        losses.update(loss_decode)
+
+        return losses
+
+    def whole_inference(self, img, img_meta, rescale, size=None):
+        """Inference with full image."""
+        depth_pred = self.encode_decode(img, img_meta, rescale, size=size)
+
+        return depth_pred
+
+    def slide_inference(self, img, img_meta, rescale):
+        """Inference by sliding-window with overlap.
+
+        If h_crop > h_img or w_crop > w_img, the small patch will be used to
+        decode without padding.
+        """
+
+        h_stride, w_stride = self.test_cfg.stride
+        h_crop, w_crop = self.test_cfg.crop_size
+        batch_size, _, h_img, w_img = img.size()
+        h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
+        w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
+        preds = img.new_zeros((batch_size, 1, h_img, w_img))
+        count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
+        for h_idx in range(h_grids):
+            for w_idx in range(w_grids):
+                y1 = h_idx * h_stride
+                x1 = w_idx * w_stride
+                y2 = min(y1 + h_crop, h_img)
+                x2 = min(x1 + w_crop, w_img)
+                y1 = max(y2 - h_crop, 0)
+                x1 = max(x2 - w_crop, 0)
+                crop_img = img[:, :, y1:y2, x1:x2]
+                depth_pred = self.encode_decode(crop_img, img_meta, rescale)
+                preds += F.pad(depth_pred, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2)))
+
+                count_mat[:, :, y1:y2, x1:x2] += 1
+        assert (count_mat == 0).sum() == 0
+        if torch.onnx.is_in_onnx_export():
+            # cast count_mat to constant while exporting to ONNX
+            count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device)
+        preds = preds / count_mat
+        return preds
+
+    def inference(self, img, img_meta, rescale, size=None):
+        """Inference with slide/whole style.
+
+        Args:
+            img (Tensor): The input image of shape (N, 3, H, W).
+            img_meta (dict): Image info dict where each dict has: 'img_shape',
+                'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            rescale (bool): Whether rescale back to original shape.
+
+        Returns:
+            Tensor: The output depth map.
+        """
+
+        assert self.test_cfg.mode in ["slide", "whole"]
+        ori_shape = img_meta[0]["ori_shape"]
+        assert all(_["ori_shape"] == ori_shape for _ in img_meta)
+        if self.test_cfg.mode == "slide":
+            depth_pred = self.slide_inference(img, img_meta, rescale)
+        else:
+            depth_pred = self.whole_inference(img, img_meta, rescale, size=size)
+        output = depth_pred
+        flip = img_meta[0]["flip"]
+        if flip:
+            flip_direction = img_meta[0]["flip_direction"]
+            assert flip_direction in ["horizontal", "vertical"]
+            if flip_direction == "horizontal":
+                output = output.flip(dims=(3,))
+            elif flip_direction == "vertical":
+                output = output.flip(dims=(2,))
+
+        return output
+
+    def simple_test(self, img, img_meta, rescale=True):
+        """Simple test with single image."""
+        depth_pred = self.inference(img, img_meta, rescale)
+        if torch.onnx.is_in_onnx_export():
+            # our inference backend only support 4D output
+            depth_pred = depth_pred.unsqueeze(0)
+            return depth_pred
+        depth_pred = depth_pred.cpu().numpy()
+        # unravel batch dim
+        depth_pred = list(depth_pred)
+        return depth_pred
+
+    def aug_test(self, imgs, img_metas, rescale=True):
+        """Test with augmentations.
+
+        Only rescale=True is supported.
+        """
+        # aug_test rescale all imgs back to ori_shape for now
+        assert rescale
+        # to save memory, we get augmented depth logit inplace
+        depth_pred = self.inference(imgs[0], img_metas[0], rescale)
+        for i in range(1, len(imgs)):
+            cur_depth_pred = self.inference(imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:])
+            depth_pred += cur_depth_pred
+        depth_pred /= len(imgs)
+        depth_pred = depth_pred.cpu().numpy()
+        # unravel batch dim
+        depth_pred = list(depth_pred)
+        return depth_pred
diff --git a/src/dinov2/eval/depth/models/losses/__init__.py b/src/dinov2/eval/depth/models/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f86242e342776da2e0acc61150d15a8d58ff1e0
--- /dev/null
+++ b/src/dinov2/eval/depth/models/losses/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .gradientloss import GradientLoss
+from .sigloss import SigLoss
diff --git a/src/dinov2/eval/depth/models/losses/gradientloss.py b/src/dinov2/eval/depth/models/losses/gradientloss.py
new file mode 100644
index 0000000000000000000000000000000000000000..1599878a6b70cdff4f8467e1e875f0d13ea89eca
--- /dev/null
+++ b/src/dinov2/eval/depth/models/losses/gradientloss.py
@@ -0,0 +1,69 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from ...models.builder import LOSSES
+
+
+@LOSSES.register_module()
+class GradientLoss(nn.Module):
+    """GradientLoss.
+
+    Adapted from https://www.cs.cornell.edu/projects/megadepth/
+
+    Args:
+        valid_mask (bool): Whether filter invalid gt (gt > 0). Default: True.
+        loss_weight (float): Weight of the loss. Default: 1.0.
+        max_depth (int): When filtering invalid gt, set a max threshold. Default: None.
+    """
+
+    def __init__(self, valid_mask=True, loss_weight=1.0, max_depth=None, loss_name="loss_grad"):
+        super(GradientLoss, self).__init__()
+        self.valid_mask = valid_mask
+        self.loss_weight = loss_weight
+        self.max_depth = max_depth
+        self.loss_name = loss_name
+
+        self.eps = 0.001  # avoid grad explode
+
+    def gradientloss(self, input, target):
+        input_downscaled = [input] + [input[:: 2 * i, :: 2 * i] for i in range(1, 4)]
+        target_downscaled = [target] + [target[:: 2 * i, :: 2 * i] for i in range(1, 4)]
+
+        gradient_loss = 0
+        for input, target in zip(input_downscaled, target_downscaled):
+            if self.valid_mask:
+                mask = target > 0
+                if self.max_depth is not None:
+                    mask = torch.logical_and(target > 0, target <= self.max_depth)
+                N = torch.sum(mask)
+            else:
+                mask = torch.ones_like(target)
+                N = input.numel()
+            input_log = torch.log(input + self.eps)
+            target_log = torch.log(target + self.eps)
+            log_d_diff = input_log - target_log
+
+            log_d_diff = torch.mul(log_d_diff, mask)
+
+            v_gradient = torch.abs(log_d_diff[0:-2, :] - log_d_diff[2:, :])
+            v_mask = torch.mul(mask[0:-2, :], mask[2:, :])
+            v_gradient = torch.mul(v_gradient, v_mask)
+
+            h_gradient = torch.abs(log_d_diff[:, 0:-2] - log_d_diff[:, 2:])
+            h_mask = torch.mul(mask[:, 0:-2], mask[:, 2:])
+            h_gradient = torch.mul(h_gradient, h_mask)
+
+            gradient_loss += (torch.sum(h_gradient) + torch.sum(v_gradient)) / N
+
+        return gradient_loss
+
+    def forward(self, depth_pred, depth_gt):
+        """Forward function."""
+
+        gradient_loss = self.loss_weight * self.gradientloss(depth_pred, depth_gt)
+        return gradient_loss
diff --git a/src/dinov2/eval/depth/models/losses/sigloss.py b/src/dinov2/eval/depth/models/losses/sigloss.py
new file mode 100644
index 0000000000000000000000000000000000000000..e12fad3e6151e4b975dd055193fdaec0206d4a14
--- /dev/null
+++ b/src/dinov2/eval/depth/models/losses/sigloss.py
@@ -0,0 +1,65 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from ...models.builder import LOSSES
+
+
+@LOSSES.register_module()
+class SigLoss(nn.Module):
+    """SigLoss.
+
+        This follows `AdaBins <https://arxiv.org/abs/2011.14141>`_.
+
+    Args:
+        valid_mask (bool): Whether filter invalid gt (gt > 0). Default: True.
+        loss_weight (float): Weight of the loss. Default: 1.0.
+        max_depth (int): When filtering invalid gt, set a max threshold. Default: None.
+        warm_up (bool): A simple warm up stage to help convergence. Default: False.
+        warm_iter (int): The number of warm up stage. Default: 100.
+    """
+
+    def __init__(
+        self, valid_mask=True, loss_weight=1.0, max_depth=None, warm_up=False, warm_iter=100, loss_name="sigloss"
+    ):
+        super(SigLoss, self).__init__()
+        self.valid_mask = valid_mask
+        self.loss_weight = loss_weight
+        self.max_depth = max_depth
+        self.loss_name = loss_name
+
+        self.eps = 0.001  # avoid grad explode
+
+        # HACK: a hack implementation for warmup sigloss
+        self.warm_up = warm_up
+        self.warm_iter = warm_iter
+        self.warm_up_counter = 0
+
+    def sigloss(self, input, target):
+        if self.valid_mask:
+            valid_mask = target > 0
+            if self.max_depth is not None:
+                valid_mask = torch.logical_and(target > 0, target <= self.max_depth)
+            input = input[valid_mask]
+            target = target[valid_mask]
+
+        if self.warm_up:
+            if self.warm_up_counter < self.warm_iter:
+                g = torch.log(input + self.eps) - torch.log(target + self.eps)
+                g = 0.15 * torch.pow(torch.mean(g), 2)
+                self.warm_up_counter += 1
+                return torch.sqrt(g)
+
+        g = torch.log(input + self.eps) - torch.log(target + self.eps)
+        Dg = torch.var(g) + 0.15 * torch.pow(torch.mean(g), 2)
+        return torch.sqrt(Dg)
+
+    def forward(self, depth_pred, depth_gt):
+        """Forward function."""
+
+        loss_depth = self.loss_weight * self.sigloss(depth_pred, depth_gt)
+        return loss_depth
diff --git a/src/dinov2/eval/depth/ops/__init__.py b/src/dinov2/eval/depth/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..78181c29581a281b5f42cf12078636aaeb43b5a5
--- /dev/null
+++ b/src/dinov2/eval/depth/ops/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .wrappers import resize
diff --git a/src/dinov2/eval/depth/ops/wrappers.py b/src/dinov2/eval/depth/ops/wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..15880ee0cb7652d4b41c489b927bf6a156b40e5e
--- /dev/null
+++ b/src/dinov2/eval/depth/ops/wrappers.py
@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import warnings
+
+import torch.nn.functional as F
+
+
+def resize(input, size=None, scale_factor=None, mode="nearest", align_corners=None, warning=False):
+    if warning:
+        if size is not None and align_corners:
+            input_h, input_w = tuple(int(x) for x in input.shape[2:])
+            output_h, output_w = tuple(int(x) for x in size)
+            if output_h > input_h or output_w > output_h:
+                if (
+                    (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1)
+                    and (output_h - 1) % (input_h - 1)
+                    and (output_w - 1) % (input_w - 1)
+                ):
+                    warnings.warn(
+                        f"When align_corners={align_corners}, "
+                        "the output would more aligned if "
+                        f"input size {(input_h, input_w)} is `x+1` and "
+                        f"out size {(output_h, output_w)} is `nx+1`"
+                    )
+    return F.interpolate(input, size, scale_factor, mode, align_corners)
diff --git a/src/dinov2/eval/knn.py b/src/dinov2/eval/knn.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3a4845da1313a6db6b8345bb9a98230fcd24acf
--- /dev/null
+++ b/src/dinov2/eval/knn.py
@@ -0,0 +1,404 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import argparse
+from functools import partial
+import json
+import logging
+import os
+import sys
+from typing import List, Optional
+
+import torch
+from torch.nn.functional import one_hot, softmax
+
+import dinov2.distributed as distributed
+from dinov2.data import SamplerType, make_data_loader, make_dataset
+from dinov2.data.transforms import make_classification_eval_transform
+from dinov2.eval.metrics import AccuracyAveraging, build_topk_accuracy_metric
+from dinov2.eval.setup import get_args_parser as get_setup_args_parser
+from dinov2.eval.setup import setup_and_build_model
+from dinov2.eval.utils import ModelWithNormalize, evaluate, extract_features
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+):
+    parents = parents or []
+    setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
+    parents = [setup_args_parser]
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents,
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--train-dataset",
+        dest="train_dataset_str",
+        type=str,
+        help="Training dataset",
+    )
+    parser.add_argument(
+        "--val-dataset",
+        dest="val_dataset_str",
+        type=str,
+        help="Validation dataset",
+    )
+    parser.add_argument(
+        "--nb_knn",
+        nargs="+",
+        type=int,
+        help="Number of NN to use. 20 is usually working the best.",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        help="Temperature used in the voting coefficient",
+    )
+    parser.add_argument(
+        "--gather-on-cpu",
+        action="store_true",
+        help="Whether to gather the train features on cpu, slower"
+        "but useful to avoid OOM for large datasets (e.g. ImageNet22k).",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        help="Batch size.",
+    )
+    parser.add_argument(
+        "--n-per-class-list",
+        nargs="+",
+        type=int,
+        help="Number to take per class",
+    )
+    parser.add_argument(
+        "--n-tries",
+        type=int,
+        help="Number of tries",
+    )
+    parser.set_defaults(
+        train_dataset_str="ImageNet:split=TRAIN",
+        val_dataset_str="ImageNet:split=VAL",
+        nb_knn=[10, 20, 100, 200],
+        temperature=0.07,
+        batch_size=256,
+        n_per_class_list=[-1],
+        n_tries=1,
+    )
+    return parser
+
+
+class KnnModule(torch.nn.Module):
+    """
+    Gets knn of test features from all processes on a chunk of the train features
+
+    Each rank gets a chunk of the train features as well as a chunk of the test features.
+    In `compute_neighbors`, for each rank one after the other, its chunk of test features
+    is sent to all devices, partial knns are computed with each chunk of train features
+    then collated back on the original device.
+    """
+
+    def __init__(self, train_features, train_labels, nb_knn, T, device, num_classes=1000):
+        super().__init__()
+
+        self.global_rank = distributed.get_global_rank()
+        self.global_size = distributed.get_global_size()
+
+        self.device = device
+        self.train_features_rank_T = train_features.chunk(self.global_size)[self.global_rank].T.to(self.device)
+        self.candidates = train_labels.chunk(self.global_size)[self.global_rank].view(1, -1).to(self.device)
+
+        self.nb_knn = nb_knn
+        self.max_k = max(self.nb_knn)
+        self.T = T
+        self.num_classes = num_classes
+
+    def _get_knn_sims_and_labels(self, similarity, train_labels):
+        topk_sims, indices = similarity.topk(self.max_k, largest=True, sorted=True)
+        neighbors_labels = torch.gather(train_labels, 1, indices)
+        return topk_sims, neighbors_labels
+
+    def _similarity_for_rank(self, features_rank, source_rank):
+        # Send the features from `source_rank` to all ranks
+        broadcast_shape = torch.tensor(features_rank.shape).to(self.device)
+        torch.distributed.broadcast(broadcast_shape, source_rank)
+
+        broadcasted = features_rank
+        if self.global_rank != source_rank:
+            broadcasted = torch.zeros(*broadcast_shape, dtype=features_rank.dtype, device=self.device)
+        torch.distributed.broadcast(broadcasted, source_rank)
+
+        # Compute the neighbors for `source_rank` among `train_features_rank_T`
+        similarity_rank = torch.mm(broadcasted, self.train_features_rank_T)
+        candidate_labels = self.candidates.expand(len(similarity_rank), -1)
+        return self._get_knn_sims_and_labels(similarity_rank, candidate_labels)
+
+    def _gather_all_knn_for_rank(self, topk_sims, neighbors_labels, target_rank):
+        # Gather all neighbors for `target_rank`
+        topk_sims_rank = retrieved_rank = None
+        if self.global_rank == target_rank:
+            topk_sims_rank = [torch.zeros_like(topk_sims) for _ in range(self.global_size)]
+            retrieved_rank = [torch.zeros_like(neighbors_labels) for _ in range(self.global_size)]
+
+        torch.distributed.gather(topk_sims, topk_sims_rank, dst=target_rank)
+        torch.distributed.gather(neighbors_labels, retrieved_rank, dst=target_rank)
+
+        if self.global_rank == target_rank:
+            # Perform a second top-k on the k * global_size retrieved neighbors
+            topk_sims_rank = torch.cat(topk_sims_rank, dim=1)
+            retrieved_rank = torch.cat(retrieved_rank, dim=1)
+            results = self._get_knn_sims_and_labels(topk_sims_rank, retrieved_rank)
+            return results
+        return None
+
+    def compute_neighbors(self, features_rank):
+        for rank in range(self.global_size):
+            topk_sims, neighbors_labels = self._similarity_for_rank(features_rank, rank)
+            results = self._gather_all_knn_for_rank(topk_sims, neighbors_labels, rank)
+            if results is not None:
+                topk_sims_rank, neighbors_labels_rank = results
+        return topk_sims_rank, neighbors_labels_rank
+
+    def forward(self, features_rank):
+        """
+        Compute the results on all values of `self.nb_knn` neighbors from the full `self.max_k`
+        """
+        assert all(k <= self.max_k for k in self.nb_knn)
+
+        topk_sims, neighbors_labels = self.compute_neighbors(features_rank)
+        batch_size = neighbors_labels.shape[0]
+        topk_sims_transform = softmax(topk_sims / self.T, 1)
+        matmul = torch.mul(
+            one_hot(neighbors_labels, num_classes=self.num_classes),
+            topk_sims_transform.view(batch_size, -1, 1),
+        )
+        probas_for_k = {k: torch.sum(matmul[:, :k, :], 1) for k in self.nb_knn}
+        return probas_for_k
+
+
+class DictKeysModule(torch.nn.Module):
+    def __init__(self, keys):
+        super().__init__()
+        self.keys = keys
+
+    def forward(self, features_dict, targets):
+        for k in self.keys:
+            features_dict = features_dict[k]
+        return {"preds": features_dict, "target": targets}
+
+
+def create_module_dict(*, module, n_per_class_list, n_tries, nb_knn, train_features, train_labels):
+    modules = {}
+    mapping = create_class_indices_mapping(train_labels)
+    for npc in n_per_class_list:
+        if npc < 0:  # Only one try needed when using the full data
+            full_module = module(
+                train_features=train_features,
+                train_labels=train_labels,
+                nb_knn=nb_knn,
+            )
+            modules["full"] = ModuleDictWithForward({"1": full_module})
+            continue
+        all_tries = {}
+        for t in range(n_tries):
+            final_indices = filter_train(mapping, npc, seed=t)
+            k_list = list(set(nb_knn + [npc]))
+            k_list = sorted([el for el in k_list if el <= npc])
+            all_tries[str(t)] = module(
+                train_features=train_features[final_indices],
+                train_labels=train_labels[final_indices],
+                nb_knn=k_list,
+            )
+        modules[f"{npc} per class"] = ModuleDictWithForward(all_tries)
+
+    return ModuleDictWithForward(modules)
+
+
+def filter_train(mapping, n_per_class, seed):
+    torch.manual_seed(seed)
+    final_indices = []
+    for k in mapping.keys():
+        index = torch.randperm(len(mapping[k]))[:n_per_class]
+        final_indices.append(mapping[k][index])
+    return torch.cat(final_indices).squeeze()
+
+
+def create_class_indices_mapping(labels):
+    unique_labels, inverse = torch.unique(labels, return_inverse=True)
+    mapping = {unique_labels[i]: (inverse == i).nonzero() for i in range(len(unique_labels))}
+    return mapping
+
+
+class ModuleDictWithForward(torch.nn.ModuleDict):
+    def forward(self, *args, **kwargs):
+        return {k: module(*args, **kwargs) for k, module in self._modules.items()}
+
+
+def eval_knn(
+    model,
+    train_dataset,
+    val_dataset,
+    accuracy_averaging,
+    nb_knn,
+    temperature,
+    batch_size,
+    num_workers,
+    gather_on_cpu,
+    n_per_class_list=[-1],
+    n_tries=1,
+):
+    model = ModelWithNormalize(model)
+
+    logger.info("Extracting features for train set...")
+    train_features, train_labels = extract_features(
+        model, train_dataset, batch_size, num_workers, gather_on_cpu=gather_on_cpu
+    )
+    logger.info(f"Train features created, shape {train_features.shape}.")
+
+    val_dataloader = make_data_loader(
+        dataset=val_dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        sampler_type=SamplerType.DISTRIBUTED,
+        drop_last=False,
+        shuffle=False,
+        persistent_workers=True,
+    )
+    num_classes = train_labels.max() + 1
+    metric_collection = build_topk_accuracy_metric(accuracy_averaging, num_classes=num_classes)
+
+    device = torch.cuda.current_device()
+    partial_module = partial(KnnModule, T=temperature, device=device, num_classes=num_classes)
+    knn_module_dict = create_module_dict(
+        module=partial_module,
+        n_per_class_list=n_per_class_list,
+        n_tries=n_tries,
+        nb_knn=nb_knn,
+        train_features=train_features,
+        train_labels=train_labels,
+    )
+    postprocessors, metrics = {}, {}
+    for n_per_class, knn_module in knn_module_dict.items():
+        for t, knn_try in knn_module.items():
+            postprocessors = {
+                **postprocessors,
+                **{(n_per_class, t, k): DictKeysModule([n_per_class, t, k]) for k in knn_try.nb_knn},
+            }
+            metrics = {**metrics, **{(n_per_class, t, k): metric_collection.clone() for k in knn_try.nb_knn}}
+    model_with_knn = torch.nn.Sequential(model, knn_module_dict)
+
+    # ============ evaluation ... ============
+    logger.info("Start the k-NN classification.")
+    _, results_dict = evaluate(model_with_knn, val_dataloader, postprocessors, metrics, device)
+
+    # Averaging the results over the n tries for each value of n_per_class
+    for n_per_class, knn_module in knn_module_dict.items():
+        first_try = list(knn_module.keys())[0]
+        k_list = knn_module[first_try].nb_knn
+        for k in k_list:
+            keys = results_dict[(n_per_class, first_try, k)].keys()  # keys are e.g. `top-1` and `top-5`
+            results_dict[(n_per_class, k)] = {
+                key: torch.mean(torch.stack([results_dict[(n_per_class, t, k)][key] for t in knn_module.keys()]))
+                for key in keys
+            }
+            for t in knn_module.keys():
+                del results_dict[(n_per_class, t, k)]
+
+    return results_dict
+
+
+def eval_knn_with_model(
+    model,
+    output_dir,
+    train_dataset_str="ImageNet:split=TRAIN",
+    val_dataset_str="ImageNet:split=VAL",
+    nb_knn=(10, 20, 100, 200),
+    temperature=0.07,
+    autocast_dtype=torch.float,
+    accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY,
+    transform=None,
+    gather_on_cpu=False,
+    batch_size=256,
+    num_workers=5,
+    n_per_class_list=[-1],
+    n_tries=1,
+):
+    transform = transform or make_classification_eval_transform()
+
+    train_dataset = make_dataset(
+        dataset_str=train_dataset_str,
+        transform=transform,
+    )
+    val_dataset = make_dataset(
+        dataset_str=val_dataset_str,
+        transform=transform,
+    )
+
+    with torch.cuda.amp.autocast(dtype=autocast_dtype):
+        results_dict_knn = eval_knn(
+            model=model,
+            train_dataset=train_dataset,
+            val_dataset=val_dataset,
+            accuracy_averaging=accuracy_averaging,
+            nb_knn=nb_knn,
+            temperature=temperature,
+            batch_size=batch_size,
+            num_workers=num_workers,
+            gather_on_cpu=gather_on_cpu,
+            n_per_class_list=n_per_class_list,
+            n_tries=n_tries,
+        )
+
+    results_dict = {}
+    if distributed.is_main_process():
+        for knn_ in results_dict_knn.keys():
+            top1 = results_dict_knn[knn_]["top-1"].item() * 100.0
+            top5 = results_dict_knn[knn_]["top-5"].item() * 100.0
+            results_dict[f"{knn_} Top 1"] = top1
+            results_dict[f"{knn_} Top 5"] = top5
+            logger.info(f"{knn_} classifier result: Top1: {top1:.2f} Top5: {top5:.2f}")
+
+    metrics_file_path = os.path.join(output_dir, "results_eval_knn.json")
+    with open(metrics_file_path, "a") as f:
+        for k, v in results_dict.items():
+            f.write(json.dumps({k: v}) + "\n")
+
+    if distributed.is_enabled():
+        torch.distributed.barrier()
+    return results_dict
+
+
+def main(args):
+    model, autocast_dtype = setup_and_build_model(args)
+    eval_knn_with_model(
+        model=model,
+        output_dir=args.output_dir,
+        train_dataset_str=args.train_dataset_str,
+        val_dataset_str=args.val_dataset_str,
+        nb_knn=args.nb_knn,
+        temperature=args.temperature,
+        autocast_dtype=autocast_dtype,
+        accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY,
+        transform=None,
+        gather_on_cpu=args.gather_on_cpu,
+        batch_size=args.batch_size,
+        num_workers=5,
+        n_per_class_list=args.n_per_class_list,
+        n_tries=args.n_tries,
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    description = "DINOv2 k-NN evaluation"
+    args_parser = get_args_parser(description=description)
+    args = args_parser.parse_args()
+    sys.exit(main(args))
diff --git a/src/dinov2/eval/linear.py b/src/dinov2/eval/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bd4c5de5a041be8a188f007257d1e91b6d6921e
--- /dev/null
+++ b/src/dinov2/eval/linear.py
@@ -0,0 +1,625 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import argparse
+from functools import partial
+import json
+import logging
+import os
+import sys
+from typing import List, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.nn.parallel import DistributedDataParallel
+from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
+
+from dinov2.data import SamplerType, make_data_loader, make_dataset
+from dinov2.data.transforms import make_classification_eval_transform, make_classification_train_transform
+import dinov2.distributed as distributed
+from dinov2.eval.metrics import MetricType, build_metric
+from dinov2.eval.setup import get_args_parser as get_setup_args_parser
+from dinov2.eval.setup import setup_and_build_model
+from dinov2.eval.utils import ModelWithIntermediateLayers, evaluate
+from dinov2.logging import MetricLogger
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+):
+    parents = parents or []
+    setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
+    parents = [setup_args_parser]
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents,
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--train-dataset",
+        dest="train_dataset_str",
+        type=str,
+        help="Training dataset",
+    )
+    parser.add_argument(
+        "--val-dataset",
+        dest="val_dataset_str",
+        type=str,
+        help="Validation dataset",
+    )
+    parser.add_argument(
+        "--test-datasets",
+        dest="test_dataset_strs",
+        type=str,
+        nargs="+",
+        help="Test datasets, none to reuse the validation dataset",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        help="Number of training epochs",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        help="Batch Size (per GPU)",
+    )
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        help="Number de Workers",
+    )
+    parser.add_argument(
+        "--epoch-length",
+        type=int,
+        help="Length of an epoch in number of iterations",
+    )
+    parser.add_argument(
+        "--save-checkpoint-frequency",
+        type=int,
+        help="Number of epochs between two named checkpoint saves.",
+    )
+    parser.add_argument(
+        "--eval-period-iterations",
+        type=int,
+        help="Number of iterations between two evaluations.",
+    )
+    parser.add_argument(
+        "--learning-rates",
+        nargs="+",
+        type=float,
+        help="Learning rates to grid search.",
+    )
+    parser.add_argument(
+        "--no-resume",
+        action="store_true",
+        help="Whether to not resume from existing checkpoints",
+    )
+    parser.add_argument(
+        "--val-metric-type",
+        type=MetricType,
+        choices=list(MetricType),
+        help="Validation metric",
+    )
+    parser.add_argument(
+        "--test-metric-types",
+        type=MetricType,
+        choices=list(MetricType),
+        nargs="+",
+        help="Evaluation metric",
+    )
+    parser.add_argument(
+        "--classifier-fpath",
+        type=str,
+        help="Path to a file containing pretrained linear classifiers",
+    )
+    parser.add_argument(
+        "--val-class-mapping-fpath",
+        type=str,
+        help="Path to a file containing a mapping to adjust classifier outputs",
+    )
+    parser.add_argument(
+        "--test-class-mapping-fpaths",
+        nargs="+",
+        type=str,
+        help="Path to a file containing a mapping to adjust classifier outputs",
+    )
+    parser.set_defaults(
+        train_dataset_str="ImageNet:split=TRAIN",
+        val_dataset_str="ImageNet:split=VAL",
+        test_dataset_strs=None,
+        epochs=10,
+        batch_size=128,
+        num_workers=8,
+        epoch_length=1250,
+        save_checkpoint_frequency=20,
+        eval_period_iterations=1250,
+        learning_rates=[1e-5, 2e-5, 5e-5, 1e-4, 2e-4, 5e-4, 1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2, 0.1],
+        val_metric_type=MetricType.MEAN_ACCURACY,
+        test_metric_types=None,
+        classifier_fpath=None,
+        val_class_mapping_fpath=None,
+        test_class_mapping_fpaths=[None],
+    )
+    return parser
+
+
+def has_ddp_wrapper(m: nn.Module) -> bool:
+    return isinstance(m, DistributedDataParallel)
+
+
+def remove_ddp_wrapper(m: nn.Module) -> nn.Module:
+    return m.module if has_ddp_wrapper(m) else m
+
+
+def _pad_and_collate(batch):
+    maxlen = max(len(targets) for image, targets in batch)
+    padded_batch = [
+        (image, np.pad(targets, (0, maxlen - len(targets)), constant_values=-1)) for image, targets in batch
+    ]
+    return torch.utils.data.default_collate(padded_batch)
+
+
+def create_linear_input(x_tokens_list, use_n_blocks, use_avgpool):
+    intermediate_output = x_tokens_list[-use_n_blocks:]
+    output = torch.cat([class_token for _, class_token in intermediate_output], dim=-1)
+    if use_avgpool:
+        output = torch.cat(
+            (
+                output,
+                torch.mean(intermediate_output[-1][0], dim=1),  # patch tokens
+            ),
+            dim=-1,
+        )
+        output = output.reshape(output.shape[0], -1)
+    return output.float()
+
+
+class LinearClassifier(nn.Module):
+    """Linear layer to train on top of frozen features"""
+
+    def __init__(self, out_dim, use_n_blocks, use_avgpool, num_classes=1000):
+        super().__init__()
+        self.out_dim = out_dim
+        self.use_n_blocks = use_n_blocks
+        self.use_avgpool = use_avgpool
+        self.num_classes = num_classes
+        self.linear = nn.Linear(out_dim, num_classes)
+        self.linear.weight.data.normal_(mean=0.0, std=0.01)
+        self.linear.bias.data.zero_()
+
+    def forward(self, x_tokens_list):
+        output = create_linear_input(x_tokens_list, self.use_n_blocks, self.use_avgpool)
+        return self.linear(output)
+
+
+class AllClassifiers(nn.Module):
+    def __init__(self, classifiers_dict):
+        super().__init__()
+        self.classifiers_dict = nn.ModuleDict()
+        self.classifiers_dict.update(classifiers_dict)
+
+    def forward(self, inputs):
+        return {k: v.forward(inputs) for k, v in self.classifiers_dict.items()}
+
+    def __len__(self):
+        return len(self.classifiers_dict)
+
+
+class LinearPostprocessor(nn.Module):
+    def __init__(self, linear_classifier, class_mapping=None):
+        super().__init__()
+        self.linear_classifier = linear_classifier
+        self.register_buffer("class_mapping", None if class_mapping is None else torch.LongTensor(class_mapping))
+
+    def forward(self, samples, targets):
+        preds = self.linear_classifier(samples)
+        return {
+            "preds": preds[:, self.class_mapping] if self.class_mapping is not None else preds,
+            "target": targets,
+        }
+
+
+def scale_lr(learning_rates, batch_size):
+    return learning_rates * (batch_size * distributed.get_global_size()) / 256.0
+
+
+def setup_linear_classifiers(sample_output, n_last_blocks_list, learning_rates, batch_size, num_classes=1000):
+    linear_classifiers_dict = nn.ModuleDict()
+    optim_param_groups = []
+    for n in n_last_blocks_list:
+        for avgpool in [False, True]:
+            for _lr in learning_rates:
+                lr = scale_lr(_lr, batch_size)
+                out_dim = create_linear_input(sample_output, use_n_blocks=n, use_avgpool=avgpool).shape[1]
+                linear_classifier = LinearClassifier(
+                    out_dim, use_n_blocks=n, use_avgpool=avgpool, num_classes=num_classes
+                )
+                linear_classifier = linear_classifier.cuda()
+                linear_classifiers_dict[
+                    f"classifier_{n}_blocks_avgpool_{avgpool}_lr_{lr:.5f}".replace(".", "_")
+                ] = linear_classifier
+                optim_param_groups.append({"params": linear_classifier.parameters(), "lr": lr})
+
+    linear_classifiers = AllClassifiers(linear_classifiers_dict)
+    if distributed.is_enabled():
+        linear_classifiers = nn.parallel.DistributedDataParallel(linear_classifiers)
+
+    return linear_classifiers, optim_param_groups
+
+
+@torch.no_grad()
+def evaluate_linear_classifiers(
+    feature_model,
+    linear_classifiers,
+    data_loader,
+    metric_type,
+    metrics_file_path,
+    training_num_classes,
+    iteration,
+    prefixstring="",
+    class_mapping=None,
+    best_classifier_on_val=None,
+):
+    logger.info("running validation !")
+
+    num_classes = len(class_mapping) if class_mapping is not None else training_num_classes
+    metric = build_metric(metric_type, num_classes=num_classes)
+    postprocessors = {k: LinearPostprocessor(v, class_mapping) for k, v in linear_classifiers.classifiers_dict.items()}
+    metrics = {k: metric.clone() for k in linear_classifiers.classifiers_dict}
+
+    _, results_dict_temp = evaluate(
+        feature_model,
+        data_loader,
+        postprocessors,
+        metrics,
+        torch.cuda.current_device(),
+    )
+
+    logger.info("")
+    results_dict = {}
+    max_accuracy = 0
+    best_classifier = ""
+    for i, (classifier_string, metric) in enumerate(results_dict_temp.items()):
+        logger.info(f"{prefixstring} -- Classifier: {classifier_string} * {metric}")
+        if (
+            best_classifier_on_val is None and metric["top-1"].item() > max_accuracy
+        ) or classifier_string == best_classifier_on_val:
+            max_accuracy = metric["top-1"].item()
+            best_classifier = classifier_string
+
+    results_dict["best_classifier"] = {"name": best_classifier, "accuracy": max_accuracy}
+
+    logger.info(f"best classifier: {results_dict['best_classifier']}")
+
+    if distributed.is_main_process():
+        with open(metrics_file_path, "a") as f:
+            f.write(f"iter: {iteration}\n")
+            for k, v in results_dict.items():
+                f.write(json.dumps({k: v}) + "\n")
+            f.write("\n")
+
+    return results_dict
+
+
+def eval_linear(
+    *,
+    feature_model,
+    linear_classifiers,
+    train_data_loader,
+    val_data_loader,
+    metrics_file_path,
+    optimizer,
+    scheduler,
+    output_dir,
+    max_iter,
+    checkpoint_period,  # In number of iter, creates a new file every period
+    running_checkpoint_period,  # Period to update main checkpoint file
+    eval_period,
+    metric_type,
+    training_num_classes,
+    resume=True,
+    classifier_fpath=None,
+    val_class_mapping=None,
+):
+    checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler)
+    start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1
+
+    periodic_checkpointer = PeriodicCheckpointer(checkpointer, checkpoint_period, max_iter=max_iter)
+    iteration = start_iter
+    logger.info("Starting training from iteration {}".format(start_iter))
+    metric_logger = MetricLogger(delimiter="  ")
+    header = "Training"
+
+    for data, labels in metric_logger.log_every(
+        train_data_loader,
+        10,
+        header,
+        max_iter,
+        start_iter,
+    ):
+        data = data.cuda(non_blocking=True)
+        labels = labels.cuda(non_blocking=True)
+
+        features = feature_model(data)
+        outputs = linear_classifiers(features)
+
+        losses = {f"loss_{k}": nn.CrossEntropyLoss()(v, labels) for k, v in outputs.items()}
+        loss = sum(losses.values())
+
+        # compute the gradients
+        optimizer.zero_grad()
+        loss.backward()
+
+        # step
+        optimizer.step()
+        scheduler.step()
+
+        # log
+        if iteration % 10 == 0:
+            torch.cuda.synchronize()
+            metric_logger.update(loss=loss.item())
+            metric_logger.update(lr=optimizer.param_groups[0]["lr"])
+            print("lr", optimizer.param_groups[0]["lr"])
+
+        if iteration - start_iter > 5:
+            if iteration % running_checkpoint_period == 0:
+                torch.cuda.synchronize()
+                if distributed.is_main_process():
+                    logger.info("Checkpointing running_checkpoint")
+                    periodic_checkpointer.save("running_checkpoint_linear_eval", iteration=iteration)
+                torch.cuda.synchronize()
+        periodic_checkpointer.step(iteration)
+
+        if eval_period > 0 and (iteration + 1) % eval_period == 0 and iteration != max_iter - 1:
+            _ = evaluate_linear_classifiers(
+                feature_model=feature_model,
+                linear_classifiers=remove_ddp_wrapper(linear_classifiers),
+                data_loader=val_data_loader,
+                metrics_file_path=metrics_file_path,
+                prefixstring=f"ITER: {iteration}",
+                metric_type=metric_type,
+                training_num_classes=training_num_classes,
+                iteration=iteration,
+                class_mapping=val_class_mapping,
+            )
+            torch.cuda.synchronize()
+
+        iteration = iteration + 1
+
+    val_results_dict = evaluate_linear_classifiers(
+        feature_model=feature_model,
+        linear_classifiers=remove_ddp_wrapper(linear_classifiers),
+        data_loader=val_data_loader,
+        metrics_file_path=metrics_file_path,
+        metric_type=metric_type,
+        training_num_classes=training_num_classes,
+        iteration=iteration,
+        class_mapping=val_class_mapping,
+    )
+    return val_results_dict, feature_model, linear_classifiers, iteration
+
+
+def make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type):
+    test_dataset = make_dataset(
+        dataset_str=test_dataset_str,
+        transform=make_classification_eval_transform(),
+    )
+    test_data_loader = make_data_loader(
+        dataset=test_dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        sampler_type=SamplerType.DISTRIBUTED,
+        drop_last=False,
+        shuffle=False,
+        persistent_workers=False,
+        collate_fn=_pad_and_collate if metric_type == MetricType.IMAGENET_REAL_ACCURACY else None,
+    )
+    return test_data_loader
+
+
+def test_on_datasets(
+    feature_model,
+    linear_classifiers,
+    test_dataset_strs,
+    batch_size,
+    num_workers,
+    test_metric_types,
+    metrics_file_path,
+    training_num_classes,
+    iteration,
+    best_classifier_on_val,
+    prefixstring="",
+    test_class_mappings=[None],
+):
+    results_dict = {}
+    for test_dataset_str, class_mapping, metric_type in zip(test_dataset_strs, test_class_mappings, test_metric_types):
+        logger.info(f"Testing on {test_dataset_str}")
+        test_data_loader = make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type)
+        dataset_results_dict = evaluate_linear_classifiers(
+            feature_model,
+            remove_ddp_wrapper(linear_classifiers),
+            test_data_loader,
+            metric_type,
+            metrics_file_path,
+            training_num_classes,
+            iteration,
+            prefixstring="",
+            class_mapping=class_mapping,
+            best_classifier_on_val=best_classifier_on_val,
+        )
+        results_dict[f"{test_dataset_str}_accuracy"] = 100.0 * dataset_results_dict["best_classifier"]["accuracy"]
+    return results_dict
+
+
+def run_eval_linear(
+    model,
+    output_dir,
+    train_dataset_str,
+    val_dataset_str,
+    batch_size,
+    epochs,
+    epoch_length,
+    num_workers,
+    save_checkpoint_frequency,
+    eval_period_iterations,
+    learning_rates,
+    autocast_dtype,
+    test_dataset_strs=None,
+    resume=True,
+    classifier_fpath=None,
+    val_class_mapping_fpath=None,
+    test_class_mapping_fpaths=[None],
+    val_metric_type=MetricType.MEAN_ACCURACY,
+    test_metric_types=None,
+):
+    seed = 0
+
+    if test_dataset_strs is None:
+        test_dataset_strs = [val_dataset_str]
+    if test_metric_types is None:
+        test_metric_types = [val_metric_type] * len(test_dataset_strs)
+    else:
+        assert len(test_metric_types) == len(test_dataset_strs)
+    assert len(test_dataset_strs) == len(test_class_mapping_fpaths)
+
+    train_transform = make_classification_train_transform()
+    train_dataset = make_dataset(
+        dataset_str=train_dataset_str,
+        transform=train_transform,
+    )
+    training_num_classes = len(torch.unique(torch.Tensor(train_dataset.get_targets().astype(int))))
+    sampler_type = SamplerType.SHARDED_INFINITE
+    # sampler_type = SamplerType.INFINITE
+
+    n_last_blocks_list = [1, 4]
+    n_last_blocks = max(n_last_blocks_list)
+    autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype)
+    feature_model = ModelWithIntermediateLayers(model, n_last_blocks, autocast_ctx)
+    sample_output = feature_model(train_dataset[0][0].unsqueeze(0).cuda())
+
+    linear_classifiers, optim_param_groups = setup_linear_classifiers(
+        sample_output,
+        n_last_blocks_list,
+        learning_rates,
+        batch_size,
+        training_num_classes,
+    )
+
+    optimizer = torch.optim.SGD(optim_param_groups, momentum=0.9, weight_decay=0)
+    max_iter = epochs * epoch_length
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_iter, eta_min=0)
+    checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler)
+    start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1
+    train_data_loader = make_data_loader(
+        dataset=train_dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        shuffle=True,
+        seed=seed,
+        sampler_type=sampler_type,
+        sampler_advance=start_iter,
+        drop_last=True,
+        persistent_workers=True,
+    )
+    val_data_loader = make_eval_data_loader(val_dataset_str, batch_size, num_workers, val_metric_type)
+
+    checkpoint_period = save_checkpoint_frequency * epoch_length
+
+    if val_class_mapping_fpath is not None:
+        logger.info(f"Using class mapping from {val_class_mapping_fpath}")
+        val_class_mapping = np.load(val_class_mapping_fpath)
+    else:
+        val_class_mapping = None
+
+    test_class_mappings = []
+    for class_mapping_fpath in test_class_mapping_fpaths:
+        if class_mapping_fpath is not None and class_mapping_fpath != "None":
+            logger.info(f"Using class mapping from {class_mapping_fpath}")
+            class_mapping = np.load(class_mapping_fpath)
+        else:
+            class_mapping = None
+        test_class_mappings.append(class_mapping)
+
+    metrics_file_path = os.path.join(output_dir, "results_eval_linear.json")
+    val_results_dict, feature_model, linear_classifiers, iteration = eval_linear(
+        feature_model=feature_model,
+        linear_classifiers=linear_classifiers,
+        train_data_loader=train_data_loader,
+        val_data_loader=val_data_loader,
+        metrics_file_path=metrics_file_path,
+        optimizer=optimizer,
+        scheduler=scheduler,
+        output_dir=output_dir,
+        max_iter=max_iter,
+        checkpoint_period=checkpoint_period,
+        running_checkpoint_period=epoch_length,
+        eval_period=eval_period_iterations,
+        metric_type=val_metric_type,
+        training_num_classes=training_num_classes,
+        resume=resume,
+        val_class_mapping=val_class_mapping,
+        classifier_fpath=classifier_fpath,
+    )
+    results_dict = {}
+    if len(test_dataset_strs) > 1 or test_dataset_strs[0] != val_dataset_str:
+        results_dict = test_on_datasets(
+            feature_model,
+            linear_classifiers,
+            test_dataset_strs,
+            batch_size,
+            0,  # num_workers,
+            test_metric_types,
+            metrics_file_path,
+            training_num_classes,
+            iteration,
+            val_results_dict["best_classifier"]["name"],
+            prefixstring="",
+            test_class_mappings=test_class_mappings,
+        )
+    results_dict["best_classifier"] = val_results_dict["best_classifier"]["name"]
+    results_dict[f"{val_dataset_str}_accuracy"] = 100.0 * val_results_dict["best_classifier"]["accuracy"]
+    logger.info("Test Results Dict " + str(results_dict))
+
+    return results_dict
+
+
+def main(args):
+    model, autocast_dtype = setup_and_build_model(args)
+    run_eval_linear(
+        model=model,
+        output_dir=args.output_dir,
+        train_dataset_str=args.train_dataset_str,
+        val_dataset_str=args.val_dataset_str,
+        test_dataset_strs=args.test_dataset_strs,
+        batch_size=args.batch_size,
+        epochs=args.epochs,
+        epoch_length=args.epoch_length,
+        num_workers=args.num_workers,
+        save_checkpoint_frequency=args.save_checkpoint_frequency,
+        eval_period_iterations=args.eval_period_iterations,
+        learning_rates=args.learning_rates,
+        autocast_dtype=autocast_dtype,
+        resume=not args.no_resume,
+        classifier_fpath=args.classifier_fpath,
+        val_metric_type=args.val_metric_type,
+        test_metric_types=args.test_metric_types,
+        val_class_mapping_fpath=args.val_class_mapping_fpath,
+        test_class_mapping_fpaths=args.test_class_mapping_fpaths,
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    description = "DINOv2 linear evaluation"
+    args_parser = get_args_parser(description=description)
+    args = args_parser.parse_args()
+    sys.exit(main(args))
diff --git a/src/dinov2/eval/log_regression.py b/src/dinov2/eval/log_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f36ec134e0ce25697428a0b3f21cdc2f0145645
--- /dev/null
+++ b/src/dinov2/eval/log_regression.py
@@ -0,0 +1,444 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import argparse
+import gc
+import logging
+import sys
+import time
+from typing import List, Optional
+
+from cuml.linear_model import LogisticRegression
+import torch
+import torch.backends.cudnn as cudnn
+import torch.distributed
+from torch import nn
+from torch.utils.data import TensorDataset
+from torchmetrics import MetricTracker
+
+from dinov2.data import make_dataset
+from dinov2.data.transforms import make_classification_eval_transform
+from dinov2.distributed import get_global_rank, get_global_size
+from dinov2.eval.metrics import MetricType, build_metric
+from dinov2.eval.setup import get_args_parser as get_setup_args_parser
+from dinov2.eval.setup import setup_and_build_model
+from dinov2.eval.utils import evaluate, extract_features
+from dinov2.utils.dtype import as_torch_dtype
+
+
+logger = logging.getLogger("dinov2")
+
+DEFAULT_MAX_ITER = 1_000
+C_POWER_RANGE = torch.linspace(-6, 5, 45)
+_CPU_DEVICE = torch.device("cpu")
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+):
+    parents = parents or []
+    setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
+    parents = [setup_args_parser]
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents,
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--train-dataset",
+        dest="train_dataset_str",
+        type=str,
+        help="Training dataset",
+    )
+    parser.add_argument(
+        "--val-dataset",
+        dest="val_dataset_str",
+        type=str,
+        help="Validation dataset",
+    )
+    parser.add_argument(
+        "--finetune-dataset-str",
+        dest="finetune_dataset_str",
+        type=str,
+        help="Fine-tuning dataset",
+    )
+    parser.add_argument(
+        "--finetune-on-val",
+        action="store_true",
+        help="If there is no finetune dataset, whether to choose the "
+        "hyperparameters on the val set instead of 10%% of the train dataset",
+    )
+    parser.add_argument(
+        "--metric-type",
+        type=MetricType,
+        choices=list(MetricType),
+        help="Metric type",
+    )
+    parser.add_argument(
+        "--train-features-device",
+        type=str,
+        help="Device to gather train features (cpu, cuda, cuda:0, etc.), default: %(default)s",
+    )
+    parser.add_argument(
+        "--train-dtype",
+        type=str,
+        help="Data type to convert the train features to (default: %(default)s)",
+    )
+    parser.add_argument(
+        "--max-train-iters",
+        type=int,
+        help="Maximum number of train iterations (default: %(default)s)",
+    )
+    parser.set_defaults(
+        train_dataset_str="ImageNet:split=TRAIN",
+        val_dataset_str="ImageNet:split=VAL",
+        finetune_dataset_str=None,
+        metric_type=MetricType.MEAN_ACCURACY,
+        train_features_device="cpu",
+        train_dtype="float64",
+        max_train_iters=DEFAULT_MAX_ITER,
+        finetune_on_val=False,
+    )
+    return parser
+
+
+class LogRegModule(nn.Module):
+    def __init__(
+        self,
+        C,
+        max_iter=DEFAULT_MAX_ITER,
+        dtype=torch.float64,
+        device=_CPU_DEVICE,
+    ):
+        super().__init__()
+        self.dtype = dtype
+        self.device = device
+        self.estimator = LogisticRegression(
+            penalty="l2",
+            C=C,
+            max_iter=max_iter,
+            output_type="numpy",
+            tol=1e-12,
+            linesearch_max_iter=50,
+        )
+
+    def forward(self, samples, targets):
+        samples_device = samples.device
+        samples = samples.to(dtype=self.dtype, device=self.device)
+        if self.device == _CPU_DEVICE:
+            samples = samples.numpy()
+        probas = self.estimator.predict_proba(samples)
+        return {"preds": torch.from_numpy(probas).to(samples_device), "target": targets}
+
+    def fit(self, train_features, train_labels):
+        train_features = train_features.to(dtype=self.dtype, device=self.device)
+        train_labels = train_labels.to(dtype=self.dtype, device=self.device)
+        if self.device == _CPU_DEVICE:
+            # both cuML and sklearn only work with numpy arrays on CPU
+            train_features = train_features.numpy()
+            train_labels = train_labels.numpy()
+        self.estimator.fit(train_features, train_labels)
+
+
+def evaluate_model(*, logreg_model, logreg_metric, test_data_loader, device):
+    postprocessors = {"metrics": logreg_model}
+    metrics = {"metrics": logreg_metric}
+    return evaluate(nn.Identity(), test_data_loader, postprocessors, metrics, device)
+
+
+def train_for_C(*, C, max_iter, train_features, train_labels, dtype=torch.float64, device=_CPU_DEVICE):
+    logreg_model = LogRegModule(C, max_iter=max_iter, dtype=dtype, device=device)
+    logreg_model.fit(train_features, train_labels)
+    return logreg_model
+
+
+def train_and_evaluate(
+    *,
+    C,
+    max_iter,
+    train_features,
+    train_labels,
+    logreg_metric,
+    test_data_loader,
+    train_dtype=torch.float64,
+    train_features_device,
+    eval_device,
+):
+    logreg_model = train_for_C(
+        C=C,
+        max_iter=max_iter,
+        train_features=train_features,
+        train_labels=train_labels,
+        dtype=train_dtype,
+        device=train_features_device,
+    )
+    return evaluate_model(
+        logreg_model=logreg_model,
+        logreg_metric=logreg_metric,
+        test_data_loader=test_data_loader,
+        device=eval_device,
+    )
+
+
+def sweep_C_values(
+    *,
+    train_features,
+    train_labels,
+    test_data_loader,
+    metric_type,
+    num_classes,
+    train_dtype=torch.float64,
+    train_features_device=_CPU_DEVICE,
+    max_train_iters=DEFAULT_MAX_ITER,
+):
+    if metric_type == MetricType.PER_CLASS_ACCURACY:
+        # If we want to output per-class accuracy, we select the hyperparameters with mean per class
+        metric_type = MetricType.MEAN_PER_CLASS_ACCURACY
+    logreg_metric = build_metric(metric_type, num_classes=num_classes)
+    metric_tracker = MetricTracker(logreg_metric, maximize=True)
+    ALL_C = 10**C_POWER_RANGE
+    logreg_models = {}
+
+    train_features = train_features.to(dtype=train_dtype, device=train_features_device)
+    train_labels = train_labels.to(device=train_features_device)
+
+    for i in range(get_global_rank(), len(ALL_C), get_global_size()):
+        C = ALL_C[i].item()
+        logger.info(
+            f"Training for C = {C:.5f}, dtype={train_dtype}, "
+            f"features: {train_features.shape}, {train_features.dtype}, "
+            f"labels: {train_labels.shape}, {train_labels.dtype}"
+        )
+        logreg_models[C] = train_for_C(
+            C=C,
+            max_iter=max_train_iters,
+            train_features=train_features,
+            train_labels=train_labels,
+            dtype=train_dtype,
+            device=train_features_device,
+        )
+
+    gather_list = [None for _ in range(get_global_size())]
+    torch.distributed.all_gather_object(gather_list, logreg_models)
+
+    logreg_models_gathered = {}
+    for logreg_dict in gather_list:
+        logreg_models_gathered.update(logreg_dict)
+
+    for i in range(len(ALL_C)):
+        metric_tracker.increment()
+        C = ALL_C[i].item()
+        evals = evaluate_model(
+            logreg_model=logreg_models_gathered[C],
+            logreg_metric=metric_tracker,
+            test_data_loader=test_data_loader,
+            device=torch.cuda.current_device(),
+        )
+        logger.info(f"Trained for C = {C:.5f}, accuracies = {evals}")
+
+        best_stats, which_epoch = metric_tracker.best_metric(return_step=True)
+        best_stats_100 = {k: 100.0 * v for k, v in best_stats.items()}
+        if which_epoch["top-1"] == i:
+            best_C = C
+    logger.info(f"Sweep best {best_stats_100}, best C = {best_C:.6f}")
+
+    return best_stats, best_C
+
+
+def eval_log_regression(
+    *,
+    model,
+    train_dataset,
+    val_dataset,
+    finetune_dataset,
+    metric_type,
+    batch_size,
+    num_workers,
+    finetune_on_val=False,
+    train_dtype=torch.float64,
+    train_features_device=_CPU_DEVICE,
+    max_train_iters=DEFAULT_MAX_ITER,
+):
+    """
+    Implements the "standard" process for log regression evaluation:
+    The value of C is chosen by training on train_dataset and evaluating on
+    finetune_dataset. Then, the final model is trained on a concatenation of
+    train_dataset and finetune_dataset, and is evaluated on val_dataset.
+    If there is no finetune_dataset, the value of C is the one that yields
+    the best results on a random 10% subset of the train dataset
+    """
+
+    start = time.time()
+
+    train_features, train_labels = extract_features(
+        model, train_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
+    )
+    val_features, val_labels = extract_features(
+        model, val_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
+    )
+    val_data_loader = torch.utils.data.DataLoader(
+        TensorDataset(val_features, val_labels),
+        batch_size=batch_size,
+        drop_last=False,
+        num_workers=0,
+        persistent_workers=False,
+    )
+
+    if finetune_dataset is None and finetune_on_val:
+        logger.info("Choosing hyperparameters on the val dataset")
+        finetune_features, finetune_labels = val_features, val_labels
+    elif finetune_dataset is None and not finetune_on_val:
+        logger.info("Choosing hyperparameters on 10% of the train dataset")
+        torch.manual_seed(0)
+        indices = torch.randperm(len(train_features), device=train_features.device)
+        finetune_index = indices[: len(train_features) // 10]
+        train_index = indices[len(train_features) // 10 :]
+        finetune_features, finetune_labels = train_features[finetune_index], train_labels[finetune_index]
+        train_features, train_labels = train_features[train_index], train_labels[train_index]
+    else:
+        logger.info("Choosing hyperparameters on the finetune dataset")
+        finetune_features, finetune_labels = extract_features(
+            model, finetune_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
+        )
+    # release the model - free GPU memory
+    del model
+    gc.collect()
+    torch.cuda.empty_cache()
+    finetune_data_loader = torch.utils.data.DataLoader(
+        TensorDataset(finetune_features, finetune_labels),
+        batch_size=batch_size,
+        drop_last=False,
+    )
+
+    if len(train_labels.shape) > 1:
+        num_classes = train_labels.shape[1]
+    else:
+        num_classes = train_labels.max() + 1
+
+    logger.info("Using cuML for logistic regression")
+
+    best_stats, best_C = sweep_C_values(
+        train_features=train_features,
+        train_labels=train_labels,
+        test_data_loader=finetune_data_loader,
+        metric_type=metric_type,
+        num_classes=num_classes,
+        train_dtype=train_dtype,
+        train_features_device=train_features_device,
+        max_train_iters=max_train_iters,
+    )
+
+    if not finetune_on_val:
+        logger.info("Best parameter found, concatenating features")
+        train_features = torch.cat((train_features, finetune_features))
+        train_labels = torch.cat((train_labels, finetune_labels))
+
+    logger.info("Training final model")
+    logreg_metric = build_metric(metric_type, num_classes=num_classes)
+    evals = train_and_evaluate(
+        C=best_C,
+        max_iter=max_train_iters,
+        train_features=train_features,
+        train_labels=train_labels,
+        logreg_metric=logreg_metric.clone(),
+        test_data_loader=val_data_loader,
+        eval_device=torch.cuda.current_device(),
+        train_dtype=train_dtype,
+        train_features_device=train_features_device,
+    )
+
+    best_stats = evals[1]["metrics"]
+
+    best_stats["best_C"] = best_C
+
+    logger.info(f"Log regression evaluation done in {int(time.time() - start)}s")
+    return best_stats
+
+
+def eval_log_regression_with_model(
+    model,
+    train_dataset_str="ImageNet:split=TRAIN",
+    val_dataset_str="ImageNet:split=VAL",
+    finetune_dataset_str=None,
+    autocast_dtype=torch.float,
+    finetune_on_val=False,
+    metric_type=MetricType.MEAN_ACCURACY,
+    train_dtype=torch.float64,
+    train_features_device=_CPU_DEVICE,
+    max_train_iters=DEFAULT_MAX_ITER,
+):
+    cudnn.benchmark = True
+
+    transform = make_classification_eval_transform(resize_size=224)
+    target_transform = None
+
+    train_dataset = make_dataset(dataset_str=train_dataset_str, transform=transform, target_transform=target_transform)
+    val_dataset = make_dataset(dataset_str=val_dataset_str, transform=transform, target_transform=target_transform)
+    if finetune_dataset_str is not None:
+        finetune_dataset = make_dataset(
+            dataset_str=finetune_dataset_str, transform=transform, target_transform=target_transform
+        )
+    else:
+        finetune_dataset = None
+
+    with torch.cuda.amp.autocast(dtype=autocast_dtype):
+        results_dict_logreg = eval_log_regression(
+            model=model,
+            train_dataset=train_dataset,
+            val_dataset=val_dataset,
+            finetune_dataset=finetune_dataset,
+            metric_type=metric_type,
+            batch_size=256,
+            num_workers=0,  # 5,
+            finetune_on_val=finetune_on_val,
+            train_dtype=train_dtype,
+            train_features_device=train_features_device,
+            max_train_iters=max_train_iters,
+        )
+
+    results_dict = {
+        "top-1": results_dict_logreg["top-1"].cpu().numpy() * 100.0,
+        "top-5": results_dict_logreg.get("top-5", torch.tensor(0.0)).cpu().numpy() * 100.0,
+        "best_C": results_dict_logreg["best_C"],
+    }
+    logger.info(
+        "\n".join(
+            [
+                "Training of the supervised logistic regression on frozen features completed.\n"
+                "Top-1 test accuracy: {acc:.1f}".format(acc=results_dict["top-1"]),
+                "Top-5 test accuracy: {acc:.1f}".format(acc=results_dict["top-5"]),
+                "obtained for C = {c:.6f}".format(c=results_dict["best_C"]),
+            ]
+        )
+    )
+
+    torch.distributed.barrier()
+    return results_dict
+
+
+def main(args):
+    model, autocast_dtype = setup_and_build_model(args)
+    eval_log_regression_with_model(
+        model=model,
+        train_dataset_str=args.train_dataset_str,
+        val_dataset_str=args.val_dataset_str,
+        finetune_dataset_str=args.finetune_dataset_str,
+        autocast_dtype=autocast_dtype,
+        finetune_on_val=args.finetune_on_val,
+        metric_type=args.metric_type,
+        train_dtype=as_torch_dtype(args.train_dtype),
+        train_features_device=torch.device(args.train_features_device),
+        max_train_iters=args.max_train_iters,
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    description = "DINOv2 logistic regression evaluation"
+    args_parser = get_args_parser(description=description)
+    args = args_parser.parse_args()
+    sys.exit(main(args))
diff --git a/src/dinov2/eval/metrics.py b/src/dinov2/eval/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..52be81a859dddde82da93c3657c35352d2bb0a48
--- /dev/null
+++ b/src/dinov2/eval/metrics.py
@@ -0,0 +1,113 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+import logging
+from typing import Any, Dict, Optional
+
+import torch
+from torch import Tensor
+from torchmetrics import Metric, MetricCollection
+from torchmetrics.classification import MulticlassAccuracy
+from torchmetrics.utilities.data import dim_zero_cat, select_topk
+
+
+logger = logging.getLogger("dinov2")
+
+
+class MetricType(Enum):
+    MEAN_ACCURACY = "mean_accuracy"
+    MEAN_PER_CLASS_ACCURACY = "mean_per_class_accuracy"
+    PER_CLASS_ACCURACY = "per_class_accuracy"
+    IMAGENET_REAL_ACCURACY = "imagenet_real_accuracy"
+
+    @property
+    def accuracy_averaging(self):
+        return getattr(AccuracyAveraging, self.name, None)
+
+    def __str__(self):
+        return self.value
+
+
+class AccuracyAveraging(Enum):
+    MEAN_ACCURACY = "micro"
+    MEAN_PER_CLASS_ACCURACY = "macro"
+    PER_CLASS_ACCURACY = "none"
+
+    def __str__(self):
+        return self.value
+
+
+def build_metric(metric_type: MetricType, *, num_classes: int, ks: Optional[tuple] = None):
+    if metric_type.accuracy_averaging is not None:
+        return build_topk_accuracy_metric(
+            average_type=metric_type.accuracy_averaging,
+            num_classes=num_classes,
+            ks=(1, 5) if ks is None else ks,
+        )
+    elif metric_type == MetricType.IMAGENET_REAL_ACCURACY:
+        return build_topk_imagenet_real_accuracy_metric(
+            num_classes=num_classes,
+            ks=(1, 5) if ks is None else ks,
+        )
+
+    raise ValueError(f"Unknown metric type {metric_type}")
+
+
+def build_topk_accuracy_metric(average_type: AccuracyAveraging, num_classes: int, ks: tuple = (1, 5)):
+    metrics: Dict[str, Metric] = {
+        f"top-{k}": MulticlassAccuracy(top_k=k, num_classes=int(num_classes), average=average_type.value) for k in ks
+    }
+    return MetricCollection(metrics)
+
+
+def build_topk_imagenet_real_accuracy_metric(num_classes: int, ks: tuple = (1, 5)):
+    metrics: Dict[str, Metric] = {f"top-{k}": ImageNetReaLAccuracy(top_k=k, num_classes=int(num_classes)) for k in ks}
+    return MetricCollection(metrics)
+
+
+class ImageNetReaLAccuracy(Metric):
+    is_differentiable: bool = False
+    higher_is_better: Optional[bool] = None
+    full_state_update: bool = False
+
+    def __init__(
+        self,
+        num_classes: int,
+        top_k: int = 1,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.num_classes = num_classes
+        self.top_k = top_k
+        self.add_state("tp", [], dist_reduce_fx="cat")
+
+    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        # preds [B, D]
+        # target [B, A]
+        # preds_oh [B, D] with 0 and 1
+        # select top K highest probabilities, use one hot representation
+        preds_oh = select_topk(preds, self.top_k)
+        # target_oh [B, D + 1] with 0 and 1
+        target_oh = torch.zeros((preds_oh.shape[0], preds_oh.shape[1] + 1), device=target.device, dtype=torch.int32)
+        target = target.long()
+        # for undefined targets (-1) use a fake value `num_classes`
+        target[target == -1] = self.num_classes
+        # fill targets, use one hot representation
+        target_oh.scatter_(1, target, 1)
+        # target_oh [B, D] (remove the fake target at index `num_classes`)
+        target_oh = target_oh[:, :-1]
+        # tp [B] with 0 and 1
+        tp = (preds_oh * target_oh == 1).sum(dim=1)
+        # at least one match between prediction and target
+        tp.clip_(max=1)
+        # ignore instances where no targets are defined
+        mask = target_oh.sum(dim=1) > 0
+        tp = tp[mask]
+        self.tp.append(tp)  # type: ignore
+
+    def compute(self) -> Tensor:
+        tp = dim_zero_cat(self.tp)  # type: ignore
+        return tp.float().mean()
diff --git a/src/dinov2/eval/segmentation/__init__.py b/src/dinov2/eval/segmentation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9
--- /dev/null
+++ b/src/dinov2/eval/segmentation/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/src/dinov2/eval/segmentation/hooks/__init__.py b/src/dinov2/eval/segmentation/hooks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..738cc2d2069521ea0353acd0cb0a03e3ddf1fa51
--- /dev/null
+++ b/src/dinov2/eval/segmentation/hooks/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .optimizer import DistOptimizerHook
diff --git a/src/dinov2/eval/segmentation/hooks/optimizer.py b/src/dinov2/eval/segmentation/hooks/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f593f26a84475bbf7ebda9607a4d10914b13a443
--- /dev/null
+++ b/src/dinov2/eval/segmentation/hooks/optimizer.py
@@ -0,0 +1,40 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+try:
+    import apex
+except ImportError:
+    print("apex is not installed")
+
+from mmcv.runner import OptimizerHook, HOOKS
+
+
+@HOOKS.register_module()
+class DistOptimizerHook(OptimizerHook):
+    """Optimizer hook for distributed training."""
+
+    def __init__(self, update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=-1, use_fp16=False):
+        self.grad_clip = grad_clip
+        self.coalesce = coalesce
+        self.bucket_size_mb = bucket_size_mb
+        self.update_interval = update_interval
+        self.use_fp16 = use_fp16
+
+    def before_run(self, runner):
+        runner.optimizer.zero_grad()
+
+    def after_train_iter(self, runner):
+        runner.outputs["loss"] /= self.update_interval
+        if self.use_fp16:
+            # runner.outputs['loss'].backward()
+            with apex.amp.scale_loss(runner.outputs["loss"], runner.optimizer) as scaled_loss:
+                scaled_loss.backward()
+        else:
+            runner.outputs["loss"].backward()
+        if self.every_n_iters(runner, self.update_interval):
+            if self.grad_clip is not None:
+                self.clip_grads(runner.model.parameters())
+            runner.optimizer.step()
+            runner.optimizer.zero_grad()
diff --git a/src/dinov2/eval/segmentation/models/__init__.py b/src/dinov2/eval/segmentation/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..88e4563d4c162d67e7900955a06bd9248d4c9a48
--- /dev/null
+++ b/src/dinov2/eval/segmentation/models/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .backbones import *  # noqa: F403
+from .decode_heads import *  # noqa: F403
diff --git a/src/dinov2/eval/segmentation/models/backbones/__init__.py b/src/dinov2/eval/segmentation/models/backbones/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..520d75bc6e064b9d64487293604ac1bda6e2b6f7
--- /dev/null
+++ b/src/dinov2/eval/segmentation/models/backbones/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .vision_transformer import DinoVisionTransformer
diff --git a/src/dinov2/eval/segmentation/models/backbones/vision_transformer.py b/src/dinov2/eval/segmentation/models/backbones/vision_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3e9753ae92a36be52f100e3004cbeeff777d14a
--- /dev/null
+++ b/src/dinov2/eval/segmentation/models/backbones/vision_transformer.py
@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from mmcv.runner import BaseModule
+from mmseg.models.builder import BACKBONES
+
+
+@BACKBONES.register_module()
+class DinoVisionTransformer(BaseModule):
+    """Vision Transformer."""
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
diff --git a/src/dinov2/eval/segmentation/models/decode_heads/__init__.py b/src/dinov2/eval/segmentation/models/decode_heads/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c55317875262dadf8970c2b3882f016b8d4731ac
--- /dev/null
+++ b/src/dinov2/eval/segmentation/models/decode_heads/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .linear_head import BNHead
diff --git a/src/dinov2/eval/segmentation/models/decode_heads/linear_head.py b/src/dinov2/eval/segmentation/models/decode_heads/linear_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1f39c68fb136f84d1aa5284da5b69581bb177cc
--- /dev/null
+++ b/src/dinov2/eval/segmentation/models/decode_heads/linear_head.py
@@ -0,0 +1,90 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from mmseg.models.builder import HEADS
+from mmseg.models.decode_heads.decode_head import BaseDecodeHead
+from mmseg.ops import resize
+
+
+@HEADS.register_module()
+class BNHead(BaseDecodeHead):
+    """Just a batchnorm."""
+
+    def __init__(self, resize_factors=None, **kwargs):
+        super().__init__(**kwargs)
+        assert self.in_channels == self.channels
+        self.bn = nn.SyncBatchNorm(self.in_channels)
+        self.resize_factors = resize_factors
+
+    def _forward_feature(self, inputs):
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
+
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
+        # print("inputs", [i.shape for i in inputs])
+        x = self._transform_inputs(inputs)
+        # print("x", x.shape)
+        feats = self.bn(x)
+        # print("feats", feats.shape)
+        return feats
+
+    def _transform_inputs(self, inputs):
+        """Transform inputs for decoder.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            Tensor: The transformed inputs
+        """
+
+        if self.input_transform == "resize_concat":
+            # accept lists (for cls token)
+            input_list = []
+            for x in inputs:
+                if isinstance(x, list):
+                    input_list.extend(x)
+                else:
+                    input_list.append(x)
+            inputs = input_list
+            # an image descriptor can be a local descriptor with resolution 1x1
+            for i, x in enumerate(inputs):
+                if len(x.shape) == 2:
+                    inputs[i] = x[:, :, None, None]
+            # select indices
+            inputs = [inputs[i] for i in self.in_index]
+            # Resizing shenanigans
+            # print("before", *(x.shape for x in inputs))
+            if self.resize_factors is not None:
+                assert len(self.resize_factors) == len(inputs), (len(self.resize_factors), len(inputs))
+                inputs = [
+                    resize(input=x, scale_factor=f, mode="bilinear" if f >= 1 else "area")
+                    for x, f in zip(inputs, self.resize_factors)
+                ]
+                # print("after", *(x.shape for x in inputs))
+            upsampled_inputs = [
+                resize(input=x, size=inputs[0].shape[2:], mode="bilinear", align_corners=self.align_corners)
+                for x in inputs
+            ]
+            inputs = torch.cat(upsampled_inputs, dim=1)
+        elif self.input_transform == "multiple_select":
+            inputs = [inputs[i] for i in self.in_index]
+        else:
+            inputs = inputs[self.in_index]
+
+        return inputs
+
+    def forward(self, inputs):
+        """Forward function."""
+        output = self._forward_feature(inputs)
+        output = self.cls_seg(output)
+        return output
diff --git a/src/dinov2/eval/segmentation/utils/__init__.py b/src/dinov2/eval/segmentation/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9
--- /dev/null
+++ b/src/dinov2/eval/segmentation/utils/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/src/dinov2/eval/segmentation/utils/colormaps.py b/src/dinov2/eval/segmentation/utils/colormaps.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6ef604b2c75792e95e438abfd51ab03d40de340
--- /dev/null
+++ b/src/dinov2/eval/segmentation/utils/colormaps.py
@@ -0,0 +1,362 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+ADE20K_COLORMAP = [
+    (0, 0, 0),
+    (120, 120, 120),
+    (180, 120, 120),
+    (6, 230, 230),
+    (80, 50, 50),
+    (4, 200, 3),
+    (120, 120, 80),
+    (140, 140, 140),
+    (204, 5, 255),
+    (230, 230, 230),
+    (4, 250, 7),
+    (224, 5, 255),
+    (235, 255, 7),
+    (150, 5, 61),
+    (120, 120, 70),
+    (8, 255, 51),
+    (255, 6, 82),
+    (143, 255, 140),
+    (204, 255, 4),
+    (255, 51, 7),
+    (204, 70, 3),
+    (0, 102, 200),
+    (61, 230, 250),
+    (255, 6, 51),
+    (11, 102, 255),
+    (255, 7, 71),
+    (255, 9, 224),
+    (9, 7, 230),
+    (220, 220, 220),
+    (255, 9, 92),
+    (112, 9, 255),
+    (8, 255, 214),
+    (7, 255, 224),
+    (255, 184, 6),
+    (10, 255, 71),
+    (255, 41, 10),
+    (7, 255, 255),
+    (224, 255, 8),
+    (102, 8, 255),
+    (255, 61, 6),
+    (255, 194, 7),
+    (255, 122, 8),
+    (0, 255, 20),
+    (255, 8, 41),
+    (255, 5, 153),
+    (6, 51, 255),
+    (235, 12, 255),
+    (160, 150, 20),
+    (0, 163, 255),
+    (140, 140, 140),
+    (250, 10, 15),
+    (20, 255, 0),
+    (31, 255, 0),
+    (255, 31, 0),
+    (255, 224, 0),
+    (153, 255, 0),
+    (0, 0, 255),
+    (255, 71, 0),
+    (0, 235, 255),
+    (0, 173, 255),
+    (31, 0, 255),
+    (11, 200, 200),
+    (255, 82, 0),
+    (0, 255, 245),
+    (0, 61, 255),
+    (0, 255, 112),
+    (0, 255, 133),
+    (255, 0, 0),
+    (255, 163, 0),
+    (255, 102, 0),
+    (194, 255, 0),
+    (0, 143, 255),
+    (51, 255, 0),
+    (0, 82, 255),
+    (0, 255, 41),
+    (0, 255, 173),
+    (10, 0, 255),
+    (173, 255, 0),
+    (0, 255, 153),
+    (255, 92, 0),
+    (255, 0, 255),
+    (255, 0, 245),
+    (255, 0, 102),
+    (255, 173, 0),
+    (255, 0, 20),
+    (255, 184, 184),
+    (0, 31, 255),
+    (0, 255, 61),
+    (0, 71, 255),
+    (255, 0, 204),
+    (0, 255, 194),
+    (0, 255, 82),
+    (0, 10, 255),
+    (0, 112, 255),
+    (51, 0, 255),
+    (0, 194, 255),
+    (0, 122, 255),
+    (0, 255, 163),
+    (255, 153, 0),
+    (0, 255, 10),
+    (255, 112, 0),
+    (143, 255, 0),
+    (82, 0, 255),
+    (163, 255, 0),
+    (255, 235, 0),
+    (8, 184, 170),
+    (133, 0, 255),
+    (0, 255, 92),
+    (184, 0, 255),
+    (255, 0, 31),
+    (0, 184, 255),
+    (0, 214, 255),
+    (255, 0, 112),
+    (92, 255, 0),
+    (0, 224, 255),
+    (112, 224, 255),
+    (70, 184, 160),
+    (163, 0, 255),
+    (153, 0, 255),
+    (71, 255, 0),
+    (255, 0, 163),
+    (255, 204, 0),
+    (255, 0, 143),
+    (0, 255, 235),
+    (133, 255, 0),
+    (255, 0, 235),
+    (245, 0, 255),
+    (255, 0, 122),
+    (255, 245, 0),
+    (10, 190, 212),
+    (214, 255, 0),
+    (0, 204, 255),
+    (20, 0, 255),
+    (255, 255, 0),
+    (0, 153, 255),
+    (0, 41, 255),
+    (0, 255, 204),
+    (41, 0, 255),
+    (41, 255, 0),
+    (173, 0, 255),
+    (0, 245, 255),
+    (71, 0, 255),
+    (122, 0, 255),
+    (0, 255, 184),
+    (0, 92, 255),
+    (184, 255, 0),
+    (0, 133, 255),
+    (255, 214, 0),
+    (25, 194, 194),
+    (102, 255, 0),
+    (92, 0, 255),
+]
+
+ADE20K_CLASS_NAMES = [
+    "",
+    "wall",
+    "building;edifice",
+    "sky",
+    "floor;flooring",
+    "tree",
+    "ceiling",
+    "road;route",
+    "bed",
+    "windowpane;window",
+    "grass",
+    "cabinet",
+    "sidewalk;pavement",
+    "person;individual;someone;somebody;mortal;soul",
+    "earth;ground",
+    "door;double;door",
+    "table",
+    "mountain;mount",
+    "plant;flora;plant;life",
+    "curtain;drape;drapery;mantle;pall",
+    "chair",
+    "car;auto;automobile;machine;motorcar",
+    "water",
+    "painting;picture",
+    "sofa;couch;lounge",
+    "shelf",
+    "house",
+    "sea",
+    "mirror",
+    "rug;carpet;carpeting",
+    "field",
+    "armchair",
+    "seat",
+    "fence;fencing",
+    "desk",
+    "rock;stone",
+    "wardrobe;closet;press",
+    "lamp",
+    "bathtub;bathing;tub;bath;tub",
+    "railing;rail",
+    "cushion",
+    "base;pedestal;stand",
+    "box",
+    "column;pillar",
+    "signboard;sign",
+    "chest;of;drawers;chest;bureau;dresser",
+    "counter",
+    "sand",
+    "sink",
+    "skyscraper",
+    "fireplace;hearth;open;fireplace",
+    "refrigerator;icebox",
+    "grandstand;covered;stand",
+    "path",
+    "stairs;steps",
+    "runway",
+    "case;display;case;showcase;vitrine",
+    "pool;table;billiard;table;snooker;table",
+    "pillow",
+    "screen;door;screen",
+    "stairway;staircase",
+    "river",
+    "bridge;span",
+    "bookcase",
+    "blind;screen",
+    "coffee;table;cocktail;table",
+    "toilet;can;commode;crapper;pot;potty;stool;throne",
+    "flower",
+    "book",
+    "hill",
+    "bench",
+    "countertop",
+    "stove;kitchen;stove;range;kitchen;range;cooking;stove",
+    "palm;palm;tree",
+    "kitchen;island",
+    "computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system",
+    "swivel;chair",
+    "boat",
+    "bar",
+    "arcade;machine",
+    "hovel;hut;hutch;shack;shanty",
+    "bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle",
+    "towel",
+    "light;light;source",
+    "truck;motortruck",
+    "tower",
+    "chandelier;pendant;pendent",
+    "awning;sunshade;sunblind",
+    "streetlight;street;lamp",
+    "booth;cubicle;stall;kiosk",
+    "television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box",
+    "airplane;aeroplane;plane",
+    "dirt;track",
+    "apparel;wearing;apparel;dress;clothes",
+    "pole",
+    "land;ground;soil",
+    "bannister;banister;balustrade;balusters;handrail",
+    "escalator;moving;staircase;moving;stairway",
+    "ottoman;pouf;pouffe;puff;hassock",
+    "bottle",
+    "buffet;counter;sideboard",
+    "poster;posting;placard;notice;bill;card",
+    "stage",
+    "van",
+    "ship",
+    "fountain",
+    "conveyer;belt;conveyor;belt;conveyer;conveyor;transporter",
+    "canopy",
+    "washer;automatic;washer;washing;machine",
+    "plaything;toy",
+    "swimming;pool;swimming;bath;natatorium",
+    "stool",
+    "barrel;cask",
+    "basket;handbasket",
+    "waterfall;falls",
+    "tent;collapsible;shelter",
+    "bag",
+    "minibike;motorbike",
+    "cradle",
+    "oven",
+    "ball",
+    "food;solid;food",
+    "step;stair",
+    "tank;storage;tank",
+    "trade;name;brand;name;brand;marque",
+    "microwave;microwave;oven",
+    "pot;flowerpot",
+    "animal;animate;being;beast;brute;creature;fauna",
+    "bicycle;bike;wheel;cycle",
+    "lake",
+    "dishwasher;dish;washer;dishwashing;machine",
+    "screen;silver;screen;projection;screen",
+    "blanket;cover",
+    "sculpture",
+    "hood;exhaust;hood",
+    "sconce",
+    "vase",
+    "traffic;light;traffic;signal;stoplight",
+    "tray",
+    "ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin",
+    "fan",
+    "pier;wharf;wharfage;dock",
+    "crt;screen",
+    "plate",
+    "monitor;monitoring;device",
+    "bulletin;board;notice;board",
+    "shower",
+    "radiator",
+    "glass;drinking;glass",
+    "clock",
+    "flag",
+]
+
+
+VOC2012_COLORMAP = [
+    (0, 0, 0),
+    (128, 0, 0),
+    (0, 128, 0),
+    (128, 128, 0),
+    (0, 0, 128),
+    (128, 0, 128),
+    (0, 128, 128),
+    (128, 128, 128),
+    (64, 0, 0),
+    (192, 0, 0),
+    (64, 128, 0),
+    (192, 128, 0),
+    (64, 0, 128),
+    (192, 0, 128),
+    (64, 128, 128),
+    (192, 128, 128),
+    (0, 64, 0),
+    (128, 64, 0),
+    (0, 192, 0),
+    (128, 192, 0),
+    (0, 64, 128),
+]
+
+
+VOC2012_CLASS_NAMES = [
+    "",
+    "aeroplane",
+    "bicycle",
+    "bird",
+    "boat",
+    "bottle",
+    "bus",
+    "car",
+    "cat",
+    "chair",
+    "cow",
+    "diningtable",
+    "dog",
+    "horse",
+    "motorbike",
+    "person",
+    "pottedplant",
+    "sheep",
+    "sofa",
+    "train",
+    "tvmonitor",
+]
diff --git a/src/dinov2/eval/segmentation_m2f/__init__.py b/src/dinov2/eval/segmentation_m2f/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c678fdf8f1dee14d7cf9be70af14e6f9a1441c3
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .core import *  # noqa: F403
+from .models import *  # noqa: F403
+from .ops import *  # noqa: F403
diff --git a/src/dinov2/eval/segmentation_m2f/core/__init__.py b/src/dinov2/eval/segmentation_m2f/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..92599806fbd221c1418d179892a0f46dc0b7d4db
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from mmseg.core.evaluation import *  # noqa: F403
+from mmseg.core.seg import *  # noqa: F403
+
+from .anchor import *  # noqa: F403
+from .box import *  # noqa: F403
+from .utils import *  # noqa: F403
diff --git a/src/dinov2/eval/segmentation_m2f/core/anchor/__init__.py b/src/dinov2/eval/segmentation_m2f/core/anchor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e71ac4d6e01462221ae01aa16d0e1231cda7e2e7
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/anchor/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .point_generator import MlvlPointGenerator  # noqa: F403
diff --git a/src/dinov2/eval/segmentation_m2f/core/anchor/builder.py b/src/dinov2/eval/segmentation_m2f/core/anchor/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dba90e22de76d2f23a86d3c057f196d55a99690
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/anchor/builder.py
@@ -0,0 +1,21 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import warnings
+
+from mmcv.utils import Registry, build_from_cfg
+
+PRIOR_GENERATORS = Registry("Generator for anchors and points")
+
+ANCHOR_GENERATORS = PRIOR_GENERATORS
+
+
+def build_prior_generator(cfg, default_args=None):
+    return build_from_cfg(cfg, PRIOR_GENERATORS, default_args)
+
+
+def build_anchor_generator(cfg, default_args=None):
+    warnings.warn("``build_anchor_generator`` would be deprecated soon, please use " "``build_prior_generator`` ")
+    return build_prior_generator(cfg, default_args=default_args)
diff --git a/src/dinov2/eval/segmentation_m2f/core/anchor/point_generator.py b/src/dinov2/eval/segmentation_m2f/core/anchor/point_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..574d71939080e22284fe99087fb2e7336657bd97
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/anchor/point_generator.py
@@ -0,0 +1,205 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torch.nn.modules.utils import _pair
+
+from .builder import PRIOR_GENERATORS
+
+
+@PRIOR_GENERATORS.register_module()
+class MlvlPointGenerator:
+    """Standard points generator for multi-level (Mlvl) feature maps in 2D
+    points-based detectors.
+
+    Args:
+        strides (list[int] | list[tuple[int, int]]): Strides of anchors
+            in multiple feature levels in order (w, h).
+        offset (float): The offset of points, the value is normalized with
+            corresponding stride. Defaults to 0.5.
+    """
+
+    def __init__(self, strides, offset=0.5):
+        self.strides = [_pair(stride) for stride in strides]
+        self.offset = offset
+
+    @property
+    def num_levels(self):
+        """int: number of feature levels that the generator will be applied"""
+        return len(self.strides)
+
+    @property
+    def num_base_priors(self):
+        """list[int]: The number of priors (points) at a point
+        on the feature grid"""
+        return [1 for _ in range(len(self.strides))]
+
+    def _meshgrid(self, x, y, row_major=True):
+        yy, xx = torch.meshgrid(y, x)
+        if row_major:
+            # warning .flatten() would cause error in ONNX exporting
+            # have to use reshape here
+            return xx.reshape(-1), yy.reshape(-1)
+
+        else:
+            return yy.reshape(-1), xx.reshape(-1)
+
+    def grid_priors(self, featmap_sizes, dtype=torch.float32, device="cuda", with_stride=False):
+        """Generate grid points of multiple feature levels.
+
+        Args:
+            featmap_sizes (list[tuple]): List of feature map sizes in
+                multiple feature levels, each size arrange as
+                as (h, w).
+            dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32.
+            device (str): The device where the anchors will be put on.
+            with_stride (bool): Whether to concatenate the stride to
+                the last dimension of points.
+
+        Return:
+            list[torch.Tensor]: Points of  multiple feature levels.
+            The sizes of each tensor should be (N, 2) when with stride is
+            ``False``, where N = width * height, width and height
+            are the sizes of the corresponding feature level,
+            and the last dimension 2 represent (coord_x, coord_y),
+            otherwise the shape should be (N, 4),
+            and the last dimension 4 represent
+            (coord_x, coord_y, stride_w, stride_h).
+        """
+
+        assert self.num_levels == len(featmap_sizes)
+        multi_level_priors = []
+        for i in range(self.num_levels):
+            priors = self.single_level_grid_priors(
+                featmap_sizes[i], level_idx=i, dtype=dtype, device=device, with_stride=with_stride
+            )
+            multi_level_priors.append(priors)
+        return multi_level_priors
+
+    def single_level_grid_priors(self, featmap_size, level_idx, dtype=torch.float32, device="cuda", with_stride=False):
+        """Generate grid Points of a single level.
+
+        Note:
+            This function is usually called by method ``self.grid_priors``.
+
+        Args:
+            featmap_size (tuple[int]): Size of the feature maps, arrange as
+                (h, w).
+            level_idx (int): The index of corresponding feature map level.
+            dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32.
+            device (str, optional): The device the tensor will be put on.
+                Defaults to 'cuda'.
+            with_stride (bool): Concatenate the stride to the last dimension
+                of points.
+
+        Return:
+            Tensor: Points of single feature levels.
+            The shape of tensor should be (N, 2) when with stride is
+            ``False``, where N = width * height, width and height
+            are the sizes of the corresponding feature level,
+            and the last dimension 2 represent (coord_x, coord_y),
+            otherwise the shape should be (N, 4),
+            and the last dimension 4 represent
+            (coord_x, coord_y, stride_w, stride_h).
+        """
+        feat_h, feat_w = featmap_size
+        stride_w, stride_h = self.strides[level_idx]
+        shift_x = (torch.arange(0, feat_w, device=device) + self.offset) * stride_w
+        # keep featmap_size as Tensor instead of int, so that we
+        # can convert to ONNX correctly
+        shift_x = shift_x.to(dtype)
+
+        shift_y = (torch.arange(0, feat_h, device=device) + self.offset) * stride_h
+        # keep featmap_size as Tensor instead of int, so that we
+        # can convert to ONNX correctly
+        shift_y = shift_y.to(dtype)
+        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
+        if not with_stride:
+            shifts = torch.stack([shift_xx, shift_yy], dim=-1)
+        else:
+            # use `shape[0]` instead of `len(shift_xx)` for ONNX export
+            stride_w = shift_xx.new_full((shift_xx.shape[0],), stride_w).to(dtype)
+            stride_h = shift_xx.new_full((shift_yy.shape[0],), stride_h).to(dtype)
+            shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h], dim=-1)
+        all_points = shifts.to(device)
+        return all_points
+
+    def valid_flags(self, featmap_sizes, pad_shape, device="cuda"):
+        """Generate valid flags of points of multiple feature levels.
+
+        Args:
+            featmap_sizes (list(tuple)): List of feature map sizes in
+                multiple feature levels, each size arrange as
+                as (h, w).
+            pad_shape (tuple(int)): The padded shape of the image,
+                 arrange as (h, w).
+            device (str): The device where the anchors will be put on.
+
+        Return:
+            list(torch.Tensor): Valid flags of points of multiple levels.
+        """
+        assert self.num_levels == len(featmap_sizes)
+        multi_level_flags = []
+        for i in range(self.num_levels):
+            point_stride = self.strides[i]
+            feat_h, feat_w = featmap_sizes[i]
+            h, w = pad_shape[:2]
+            valid_feat_h = min(int(np.ceil(h / point_stride[1])), feat_h)
+            valid_feat_w = min(int(np.ceil(w / point_stride[0])), feat_w)
+            flags = self.single_level_valid_flags((feat_h, feat_w), (valid_feat_h, valid_feat_w), device=device)
+            multi_level_flags.append(flags)
+        return multi_level_flags
+
+    def single_level_valid_flags(self, featmap_size, valid_size, device="cuda"):
+        """Generate the valid flags of points of a single feature map.
+
+        Args:
+            featmap_size (tuple[int]): The size of feature maps, arrange as
+                as (h, w).
+            valid_size (tuple[int]): The valid size of the feature maps.
+                The size arrange as as (h, w).
+            device (str, optional): The device where the flags will be put on.
+                Defaults to 'cuda'.
+
+        Returns:
+            torch.Tensor: The valid flags of each points in a single level \
+                feature map.
+        """
+        feat_h, feat_w = featmap_size
+        valid_h, valid_w = valid_size
+        assert valid_h <= feat_h and valid_w <= feat_w
+        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
+        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
+        valid_x[:valid_w] = 1
+        valid_y[:valid_h] = 1
+        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
+        valid = valid_xx & valid_yy
+        return valid
+
+    def sparse_priors(self, prior_idxs, featmap_size, level_idx, dtype=torch.float32, device="cuda"):
+        """Generate sparse points according to the ``prior_idxs``.
+
+        Args:
+            prior_idxs (Tensor): The index of corresponding anchors
+                in the feature map.
+            featmap_size (tuple[int]): feature map size arrange as (w, h).
+            level_idx (int): The level index of corresponding feature
+                map.
+            dtype (obj:`torch.dtype`): Date type of points. Defaults to
+                ``torch.float32``.
+            device (obj:`torch.device`): The device where the points is
+                located.
+        Returns:
+            Tensor: Anchor with shape (N, 2), N should be equal to
+            the length of ``prior_idxs``. And last dimension
+            2 represent (coord_x, coord_y).
+        """
+        height, width = featmap_size
+        x = (prior_idxs % width + self.offset) * self.strides[level_idx][0]
+        y = ((prior_idxs // width) % height + self.offset) * self.strides[level_idx][1]
+        prioris = torch.stack([x, y], 1).to(dtype)
+        prioris = prioris.to(device)
+        return prioris
diff --git a/src/dinov2/eval/segmentation_m2f/core/box/__init__.py b/src/dinov2/eval/segmentation_m2f/core/box/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf35a613f81acd77ecab2dfb75a722fa8e5c0787
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/box/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .builder import *  # noqa: F403
+from .samplers import MaskPseudoSampler  # noqa: F403
diff --git a/src/dinov2/eval/segmentation_m2f/core/box/builder.py b/src/dinov2/eval/segmentation_m2f/core/box/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..9538c0de3db682c2b111b085a8a1ce321c76a9ff
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/box/builder.py
@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from mmcv.utils import Registry, build_from_cfg
+
+BBOX_SAMPLERS = Registry("bbox_sampler")
+BBOX_CODERS = Registry("bbox_coder")
+
+
+def build_sampler(cfg, **default_args):
+    """Builder of box sampler."""
+    return build_from_cfg(cfg, BBOX_SAMPLERS, default_args)
+
+
+def build_bbox_coder(cfg, **default_args):
+    """Builder of box coder."""
+    return build_from_cfg(cfg, BBOX_CODERS, default_args)
diff --git a/src/dinov2/eval/segmentation_m2f/core/box/samplers/__init__.py b/src/dinov2/eval/segmentation_m2f/core/box/samplers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..19c363e3fabc365d92aeaf1e78189d710db279e9
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/box/samplers/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .mask_pseudo_sampler import MaskPseudoSampler  # noqa: F403
diff --git a/src/dinov2/eval/segmentation_m2f/core/box/samplers/base_sampler.py b/src/dinov2/eval/segmentation_m2f/core/box/samplers/base_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..c45cec3ed7af5b49bb54b92d6e6bcf59b06b4c99
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/box/samplers/base_sampler.py
@@ -0,0 +1,92 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from abc import ABCMeta, abstractmethod
+
+import torch
+
+from .sampling_result import SamplingResult
+
+
+class BaseSampler(metaclass=ABCMeta):
+    """Base class of samplers."""
+
+    def __init__(self, num, pos_fraction, neg_pos_ub=-1, add_gt_as_proposals=True, **kwargs):
+        self.num = num
+        self.pos_fraction = pos_fraction
+        self.neg_pos_ub = neg_pos_ub
+        self.add_gt_as_proposals = add_gt_as_proposals
+        self.pos_sampler = self
+        self.neg_sampler = self
+
+    @abstractmethod
+    def _sample_pos(self, assign_result, num_expected, **kwargs):
+        """Sample positive samples."""
+        pass
+
+    @abstractmethod
+    def _sample_neg(self, assign_result, num_expected, **kwargs):
+        """Sample negative samples."""
+        pass
+
+    def sample(self, assign_result, bboxes, gt_bboxes, gt_labels=None, **kwargs):
+        """Sample positive and negative bboxes.
+
+        This is a simple implementation of bbox sampling given candidates,
+        assigning results and ground truth bboxes.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Bbox assigning results.
+            bboxes (Tensor): Boxes to be sampled from.
+            gt_bboxes (Tensor): Ground truth bboxes.
+            gt_labels (Tensor, optional): Class labels of ground truth bboxes.
+
+        Returns:
+            :obj:`SamplingResult`: Sampling result.
+
+        Example:
+            >>> from mmdet.core.bbox import RandomSampler
+            >>> from mmdet.core.bbox import AssignResult
+            >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes
+            >>> rng = ensure_rng(None)
+            >>> assign_result = AssignResult.random(rng=rng)
+            >>> bboxes = random_boxes(assign_result.num_preds, rng=rng)
+            >>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng)
+            >>> gt_labels = None
+            >>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1,
+            >>>                      add_gt_as_proposals=False)
+            >>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels)
+        """
+        if len(bboxes.shape) < 2:
+            bboxes = bboxes[None, :]
+
+        bboxes = bboxes[:, :4]
+
+        gt_flags = bboxes.new_zeros((bboxes.shape[0],), dtype=torch.uint8)
+        if self.add_gt_as_proposals and len(gt_bboxes) > 0:
+            if gt_labels is None:
+                raise ValueError("gt_labels must be given when add_gt_as_proposals is True")
+            bboxes = torch.cat([gt_bboxes, bboxes], dim=0)
+            assign_result.add_gt_(gt_labels)
+            gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8)
+            gt_flags = torch.cat([gt_ones, gt_flags])
+
+        num_expected_pos = int(self.num * self.pos_fraction)
+        pos_inds = self.pos_sampler._sample_pos(assign_result, num_expected_pos, bboxes=bboxes, **kwargs)
+        # We found that sampled indices have duplicated items occasionally.
+        # (may be a bug of PyTorch)
+        pos_inds = pos_inds.unique()
+        num_sampled_pos = pos_inds.numel()
+        num_expected_neg = self.num - num_sampled_pos
+        if self.neg_pos_ub >= 0:
+            _pos = max(1, num_sampled_pos)
+            neg_upper_bound = int(self.neg_pos_ub * _pos)
+            if num_expected_neg > neg_upper_bound:
+                num_expected_neg = neg_upper_bound
+        neg_inds = self.neg_sampler._sample_neg(assign_result, num_expected_neg, bboxes=bboxes, **kwargs)
+        neg_inds = neg_inds.unique()
+
+        sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags)
+        return sampling_result
diff --git a/src/dinov2/eval/segmentation_m2f/core/box/samplers/mask_pseudo_sampler.py b/src/dinov2/eval/segmentation_m2f/core/box/samplers/mask_pseudo_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e67ea61ed0fd65cca0addde1893a3c1e176bf15
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/box/samplers/mask_pseudo_sampler.py
@@ -0,0 +1,45 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py
+
+import torch
+
+from ..builder import BBOX_SAMPLERS
+from .base_sampler import BaseSampler
+from .mask_sampling_result import MaskSamplingResult
+
+
+@BBOX_SAMPLERS.register_module()
+class MaskPseudoSampler(BaseSampler):
+    """A pseudo sampler that does not do sampling actually."""
+
+    def __init__(self, **kwargs):
+        pass
+
+    def _sample_pos(self, **kwargs):
+        """Sample positive samples."""
+        raise NotImplementedError
+
+    def _sample_neg(self, **kwargs):
+        """Sample negative samples."""
+        raise NotImplementedError
+
+    def sample(self, assign_result, masks, gt_masks, **kwargs):
+        """Directly returns the positive and negative indices  of samples.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Assigned results
+            masks (torch.Tensor): Bounding boxes
+            gt_masks (torch.Tensor): Ground truth boxes
+        Returns:
+            :obj:`SamplingResult`: sampler results
+        """
+        pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique()
+        neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique()
+        gt_flags = masks.new_zeros(masks.shape[0], dtype=torch.uint8)
+        sampling_result = MaskSamplingResult(pos_inds, neg_inds, masks, gt_masks, assign_result, gt_flags)
+        return sampling_result
diff --git a/src/dinov2/eval/segmentation_m2f/core/box/samplers/mask_sampling_result.py b/src/dinov2/eval/segmentation_m2f/core/box/samplers/mask_sampling_result.py
new file mode 100644
index 0000000000000000000000000000000000000000..270ffd35a5f120dd0560a7fea7fe83ef0bab66bb
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/box/samplers/mask_sampling_result.py
@@ -0,0 +1,63 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py
+
+import torch
+
+from .sampling_result import SamplingResult
+
+
+class MaskSamplingResult(SamplingResult):
+    """Mask sampling result."""
+
+    def __init__(self, pos_inds, neg_inds, masks, gt_masks, assign_result, gt_flags):
+        self.pos_inds = pos_inds
+        self.neg_inds = neg_inds
+        self.pos_masks = masks[pos_inds]
+        self.neg_masks = masks[neg_inds]
+        self.pos_is_gt = gt_flags[pos_inds]
+
+        self.num_gts = gt_masks.shape[0]
+        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
+
+        if gt_masks.numel() == 0:
+            # hack for index error case
+            assert self.pos_assigned_gt_inds.numel() == 0
+            self.pos_gt_masks = torch.empty_like(gt_masks)
+        else:
+            self.pos_gt_masks = gt_masks[self.pos_assigned_gt_inds, :]
+
+        if assign_result.labels is not None:
+            self.pos_gt_labels = assign_result.labels[pos_inds]
+        else:
+            self.pos_gt_labels = None
+
+    @property
+    def masks(self):
+        """torch.Tensor: concatenated positive and negative boxes"""
+        return torch.cat([self.pos_masks, self.neg_masks])
+
+    def __nice__(self):
+        data = self.info.copy()
+        data["pos_masks"] = data.pop("pos_masks").shape
+        data["neg_masks"] = data.pop("neg_masks").shape
+        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
+        body = "    " + ",\n    ".join(parts)
+        return "{\n" + body + "\n}"
+
+    @property
+    def info(self):
+        """Returns a dictionary of info about the object."""
+        return {
+            "pos_inds": self.pos_inds,
+            "neg_inds": self.neg_inds,
+            "pos_masks": self.pos_masks,
+            "neg_masks": self.neg_masks,
+            "pos_is_gt": self.pos_is_gt,
+            "num_gts": self.num_gts,
+            "pos_assigned_gt_inds": self.pos_assigned_gt_inds,
+        }
diff --git a/src/dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py b/src/dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaee3fe55aeb8c6da7edefbbd382d94b67b6a6b4
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py
@@ -0,0 +1,152 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+
+
+class SamplingResult:
+    """Bbox sampling result.
+
+    Example:
+        >>> # xdoctest: +IGNORE_WANT
+        >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
+        >>> self = SamplingResult.random(rng=10)
+        >>> print(f'self = {self}')
+        self = <SamplingResult({
+            'neg_bboxes': torch.Size([12, 4]),
+            'neg_inds': tensor([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
+            'num_gts': 4,
+            'pos_assigned_gt_inds': tensor([], dtype=torch.int64),
+            'pos_bboxes': torch.Size([0, 4]),
+            'pos_inds': tensor([], dtype=torch.int64),
+            'pos_is_gt': tensor([], dtype=torch.uint8)
+        })>
+    """
+
+    def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags):
+        self.pos_inds = pos_inds
+        self.neg_inds = neg_inds
+        self.pos_bboxes = bboxes[pos_inds]
+        self.neg_bboxes = bboxes[neg_inds]
+        self.pos_is_gt = gt_flags[pos_inds]
+
+        self.num_gts = gt_bboxes.shape[0]
+        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
+
+        if gt_bboxes.numel() == 0:
+            # hack for index error case
+            assert self.pos_assigned_gt_inds.numel() == 0
+            self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4)
+        else:
+            if len(gt_bboxes.shape) < 2:
+                gt_bboxes = gt_bboxes.view(-1, 4)
+
+            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds.long(), :]
+
+        if assign_result.labels is not None:
+            self.pos_gt_labels = assign_result.labels[pos_inds]
+        else:
+            self.pos_gt_labels = None
+
+    @property
+    def bboxes(self):
+        """torch.Tensor: concatenated positive and negative boxes"""
+        return torch.cat([self.pos_bboxes, self.neg_bboxes])
+
+    def to(self, device):
+        """Change the device of the data inplace.
+
+        Example:
+            >>> self = SamplingResult.random()
+            >>> print(f'self = {self.to(None)}')
+            >>> # xdoctest: +REQUIRES(--gpu)
+            >>> print(f'self = {self.to(0)}')
+        """
+        _dict = self.__dict__
+        for key, value in _dict.items():
+            if isinstance(value, torch.Tensor):
+                _dict[key] = value.to(device)
+        return self
+
+    def __nice__(self):
+        data = self.info.copy()
+        data["pos_bboxes"] = data.pop("pos_bboxes").shape
+        data["neg_bboxes"] = data.pop("neg_bboxes").shape
+        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
+        body = "    " + ",\n    ".join(parts)
+        return "{\n" + body + "\n}"
+
+    @property
+    def info(self):
+        """Returns a dictionary of info about the object."""
+        return {
+            "pos_inds": self.pos_inds,
+            "neg_inds": self.neg_inds,
+            "pos_bboxes": self.pos_bboxes,
+            "neg_bboxes": self.neg_bboxes,
+            "pos_is_gt": self.pos_is_gt,
+            "num_gts": self.num_gts,
+            "pos_assigned_gt_inds": self.pos_assigned_gt_inds,
+        }
+
+    @classmethod
+    def random(cls, rng=None, **kwargs):
+        """
+        Args:
+            rng (None | int | numpy.random.RandomState): seed or state.
+            kwargs (keyword arguments):
+                - num_preds: number of predicted boxes
+                - num_gts: number of true boxes
+                - p_ignore (float): probability of a predicted box assigned to \
+                    an ignored truth.
+                - p_assigned (float): probability of a predicted box not being \
+                    assigned.
+                - p_use_label (float | bool): with labels or not.
+
+        Returns:
+            :obj:`SamplingResult`: Randomly generated sampling result.
+
+        Example:
+            >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
+            >>> self = SamplingResult.random()
+            >>> print(self.__dict__)
+        """
+        from mmdet.core.bbox import demodata
+        from mmdet.core.bbox.assigners.assign_result import AssignResult
+        from mmdet.core.bbox.samplers.random_sampler import RandomSampler
+
+        rng = demodata.ensure_rng(rng)
+
+        # make probabalistic?
+        num = 32
+        pos_fraction = 0.5
+        neg_pos_ub = -1
+
+        assign_result = AssignResult.random(rng=rng, **kwargs)
+
+        # Note we could just compute an assignment
+        bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng)
+        gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng)
+
+        if rng.rand() > 0.2:
+            # sometimes algorithms squeeze their data, be robust to that
+            gt_bboxes = gt_bboxes.squeeze()
+            bboxes = bboxes.squeeze()
+
+        if assign_result.labels is None:
+            gt_labels = None
+        else:
+            gt_labels = None
+
+        if gt_labels is None:
+            add_gt_as_proposals = False
+        else:
+            add_gt_as_proposals = True  # make probabalistic?
+
+        sampler = RandomSampler(
+            num, pos_fraction, neg_pos_ub=neg_pos_ub, add_gt_as_proposals=add_gt_as_proposals, rng=rng
+        )
+        self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
+        return self
diff --git a/src/dinov2/eval/segmentation_m2f/core/utils/__init__.py b/src/dinov2/eval/segmentation_m2f/core/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cdc9e19352f50bc2d5433c412ff71186c5df019
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/utils/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .dist_utils import reduce_mean
+from .misc import add_prefix, multi_apply
diff --git a/src/dinov2/eval/segmentation_m2f/core/utils/dist_utils.py b/src/dinov2/eval/segmentation_m2f/core/utils/dist_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dfed42da821cd94e31b663d86b20b8f09799b30
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/utils/dist_utils.py
@@ -0,0 +1,15 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch.distributed as dist
+
+
+def reduce_mean(tensor):
+    """ "Obtain the mean of tensor on different GPUs."""
+    if not (dist.is_available() and dist.is_initialized()):
+        return tensor
+    tensor = tensor.clone()
+    dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
+    return tensor
diff --git a/src/dinov2/eval/segmentation_m2f/core/utils/misc.py b/src/dinov2/eval/segmentation_m2f/core/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..e07579e7b182b62153e81fe637ffd0f3081ef2a3
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/core/utils/misc.py
@@ -0,0 +1,47 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from functools import partial
+
+
+def multi_apply(func, *args, **kwargs):
+    """Apply function to a list of arguments.
+
+    Note:
+        This function applies the ``func`` to multiple inputs and
+        map the multiple outputs of the ``func`` into different
+        list. Each list contains the same type of outputs corresponding
+        to different inputs.
+
+    Args:
+        func (Function): A function that will be applied to a list of
+            arguments
+
+    Returns:
+        tuple(list): A tuple containing multiple list, each list contains \
+            a kind of returned results by the function
+    """
+    pfunc = partial(func, **kwargs) if kwargs else func
+    map_results = map(pfunc, *args)
+    return tuple(map(list, zip(*map_results)))
+
+
+def add_prefix(inputs, prefix):
+    """Add prefix for dict.
+
+    Args:
+        inputs (dict): The input dict with str keys.
+        prefix (str): The prefix to add.
+
+    Returns:
+
+        dict: The dict with keys updated with ``prefix``.
+    """
+
+    outputs = dict()
+    for name, value in inputs.items():
+        outputs[f"{prefix}.{name}"] = value
+
+    return outputs
diff --git a/src/dinov2/eval/segmentation_m2f/models/__init__.py b/src/dinov2/eval/segmentation_m2f/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed89bb0064d82b4360af020798eab3d2f5a47937
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .backbones import *  # noqa: F403
+from .builder import MASK_ASSIGNERS, MATCH_COST, TRANSFORMER, build_assigner, build_match_cost
+from .decode_heads import *  # noqa: F403
+from .losses import *  # noqa: F403
+from .plugins import *  # noqa: F403
+from .segmentors import *  # noqa: F403
diff --git a/src/dinov2/eval/segmentation_m2f/models/backbones/__init__.py b/src/dinov2/eval/segmentation_m2f/models/backbones/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4bf73bcbcee710676f81cb6517ae787f4d61cc6
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/backbones/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .vit_adapter import ViTAdapter
diff --git a/src/dinov2/eval/segmentation_m2f/models/backbones/adapter_modules.py b/src/dinov2/eval/segmentation_m2f/models/backbones/adapter_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..26bfdf8f6ae6c107d22d61985cce34d4b5ce275f
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/backbones/adapter_modules.py
@@ -0,0 +1,442 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+
+from ...ops.modules import MSDeformAttn
+from .drop_path import DropPath
+
+
+def get_reference_points(spatial_shapes, device):
+    reference_points_list = []
+    for lvl, (H_, W_) in enumerate(spatial_shapes):
+        ref_y, ref_x = torch.meshgrid(
+            torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
+            torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
+        )
+        ref_y = ref_y.reshape(-1)[None] / H_
+        ref_x = ref_x.reshape(-1)[None] / W_
+        ref = torch.stack((ref_x, ref_y), -1)
+        reference_points_list.append(ref)
+    reference_points = torch.cat(reference_points_list, 1)
+    reference_points = reference_points[:, :, None]
+    return reference_points
+
+
+def deform_inputs(x, patch_size):
+    bs, c, h, w = x.shape
+    spatial_shapes = torch.as_tensor(
+        [(h // 8, w // 8), (h // 16, w // 16), (h // 32, w // 32)], dtype=torch.long, device=x.device
+    )
+    level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
+    reference_points = get_reference_points([(h // patch_size, w // patch_size)], x.device)
+    deform_inputs1 = [reference_points, spatial_shapes, level_start_index]
+
+    spatial_shapes = torch.as_tensor([(h // patch_size, w // patch_size)], dtype=torch.long, device=x.device)
+    level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
+    reference_points = get_reference_points([(h // 8, w // 8), (h // 16, w // 16), (h // 32, w // 32)], x.device)
+    deform_inputs2 = [reference_points, spatial_shapes, level_start_index]
+
+    return deform_inputs1, deform_inputs2
+
+
+class ConvFFN(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.dwconv = DWConv(hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x, H, W):
+        x = self.fc1(x)
+        x = self.dwconv(x, H, W)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class DWConv(nn.Module):
+    def __init__(self, dim=768):
+        super().__init__()
+        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
+
+    def forward(self, x, H, W):
+        B, N, C = x.shape
+        n = N // 21
+        x1 = x[:, 0 : 16 * n, :].transpose(1, 2).view(B, C, H * 2, W * 2).contiguous()
+        x2 = x[:, 16 * n : 20 * n, :].transpose(1, 2).view(B, C, H, W).contiguous()
+        x3 = x[:, 20 * n :, :].transpose(1, 2).view(B, C, H // 2, W // 2).contiguous()
+        x1 = self.dwconv(x1).flatten(2).transpose(1, 2)
+        x2 = self.dwconv(x2).flatten(2).transpose(1, 2)
+        x3 = self.dwconv(x3).flatten(2).transpose(1, 2)
+        x = torch.cat([x1, x2, x3], dim=1)
+        return x
+
+
+class Extractor(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=6,
+        n_points=4,
+        n_levels=1,
+        deform_ratio=1.0,
+        with_cffn=True,
+        cffn_ratio=0.25,
+        drop=0.0,
+        drop_path=0.0,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        with_cp=False,
+    ):
+        super().__init__()
+        self.query_norm = norm_layer(dim)
+        self.feat_norm = norm_layer(dim)
+        self.attn = MSDeformAttn(
+            d_model=dim, n_levels=n_levels, n_heads=num_heads, n_points=n_points, ratio=deform_ratio
+        )
+        self.with_cffn = with_cffn
+        self.with_cp = with_cp
+        if with_cffn:
+            self.ffn = ConvFFN(in_features=dim, hidden_features=int(dim * cffn_ratio), drop=drop)
+            self.ffn_norm = norm_layer(dim)
+            self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, query, reference_points, feat, spatial_shapes, level_start_index, H, W):
+        def _inner_forward(query, feat):
+
+            attn = self.attn(
+                self.query_norm(query), reference_points, self.feat_norm(feat), spatial_shapes, level_start_index, None
+            )
+            query = query + attn
+
+            if self.with_cffn:
+                query = query + self.drop_path(self.ffn(self.ffn_norm(query), H, W))
+            return query
+
+        if self.with_cp and query.requires_grad:
+            query = cp.checkpoint(_inner_forward, query, feat)
+        else:
+            query = _inner_forward(query, feat)
+
+        return query
+
+
+class Injector(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=6,
+        n_points=4,
+        n_levels=1,
+        deform_ratio=1.0,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        init_values=0.0,
+        with_cp=False,
+    ):
+        super().__init__()
+        self.with_cp = with_cp
+        self.query_norm = norm_layer(dim)
+        self.feat_norm = norm_layer(dim)
+        self.attn = MSDeformAttn(
+            d_model=dim, n_levels=n_levels, n_heads=num_heads, n_points=n_points, ratio=deform_ratio
+        )
+        self.gamma = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+
+    def forward(self, query, reference_points, feat, spatial_shapes, level_start_index):
+        def _inner_forward(query, feat):
+
+            attn = self.attn(
+                self.query_norm(query), reference_points, self.feat_norm(feat), spatial_shapes, level_start_index, None
+            )
+            return query + self.gamma * attn
+
+        if self.with_cp and query.requires_grad:
+            query = cp.checkpoint(_inner_forward, query, feat)
+        else:
+            query = _inner_forward(query, feat)
+
+        return query
+
+
+class InteractionBlock(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=6,
+        n_points=4,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        drop=0.0,
+        drop_path=0.0,
+        with_cffn=True,
+        cffn_ratio=0.25,
+        init_values=0.0,
+        deform_ratio=1.0,
+        extra_extractor=False,
+        with_cp=False,
+    ):
+        super().__init__()
+
+        self.injector = Injector(
+            dim=dim,
+            n_levels=3,
+            num_heads=num_heads,
+            init_values=init_values,
+            n_points=n_points,
+            norm_layer=norm_layer,
+            deform_ratio=deform_ratio,
+            with_cp=with_cp,
+        )
+        self.extractor = Extractor(
+            dim=dim,
+            n_levels=1,
+            num_heads=num_heads,
+            n_points=n_points,
+            norm_layer=norm_layer,
+            deform_ratio=deform_ratio,
+            with_cffn=with_cffn,
+            cffn_ratio=cffn_ratio,
+            drop=drop,
+            drop_path=drop_path,
+            with_cp=with_cp,
+        )
+        if extra_extractor:
+            self.extra_extractors = nn.Sequential(
+                *[
+                    Extractor(
+                        dim=dim,
+                        num_heads=num_heads,
+                        n_points=n_points,
+                        norm_layer=norm_layer,
+                        with_cffn=with_cffn,
+                        cffn_ratio=cffn_ratio,
+                        deform_ratio=deform_ratio,
+                        drop=drop,
+                        drop_path=drop_path,
+                        with_cp=with_cp,
+                    )
+                    for _ in range(2)
+                ]
+            )
+        else:
+            self.extra_extractors = None
+
+    def forward(self, x, c, blocks, deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks):
+        x = self.injector(
+            query=x,
+            reference_points=deform_inputs1[0],
+            feat=c,
+            spatial_shapes=deform_inputs1[1],
+            level_start_index=deform_inputs1[2],
+        )
+        for idx, blk in enumerate(blocks):
+            x = blk(x, H_toks, W_toks)
+        c = self.extractor(
+            query=c,
+            reference_points=deform_inputs2[0],
+            feat=x,
+            spatial_shapes=deform_inputs2[1],
+            level_start_index=deform_inputs2[2],
+            H=H_c,
+            W=W_c,
+        )
+        if self.extra_extractors is not None:
+            for extractor in self.extra_extractors:
+                c = extractor(
+                    query=c,
+                    reference_points=deform_inputs2[0],
+                    feat=x,
+                    spatial_shapes=deform_inputs2[1],
+                    level_start_index=deform_inputs2[2],
+                    H=H_c,
+                    W=W_c,
+                )
+        return x, c
+
+
+class InteractionBlockWithCls(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=6,
+        n_points=4,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        drop=0.0,
+        drop_path=0.0,
+        with_cffn=True,
+        cffn_ratio=0.25,
+        init_values=0.0,
+        deform_ratio=1.0,
+        extra_extractor=False,
+        with_cp=False,
+    ):
+        super().__init__()
+
+        self.injector = Injector(
+            dim=dim,
+            n_levels=3,
+            num_heads=num_heads,
+            init_values=init_values,
+            n_points=n_points,
+            norm_layer=norm_layer,
+            deform_ratio=deform_ratio,
+            with_cp=with_cp,
+        )
+        self.extractor = Extractor(
+            dim=dim,
+            n_levels=1,
+            num_heads=num_heads,
+            n_points=n_points,
+            norm_layer=norm_layer,
+            deform_ratio=deform_ratio,
+            with_cffn=with_cffn,
+            cffn_ratio=cffn_ratio,
+            drop=drop,
+            drop_path=drop_path,
+            with_cp=with_cp,
+        )
+        if extra_extractor:
+            self.extra_extractors = nn.Sequential(
+                *[
+                    Extractor(
+                        dim=dim,
+                        num_heads=num_heads,
+                        n_points=n_points,
+                        norm_layer=norm_layer,
+                        with_cffn=with_cffn,
+                        cffn_ratio=cffn_ratio,
+                        deform_ratio=deform_ratio,
+                        drop=drop,
+                        drop_path=drop_path,
+                        with_cp=with_cp,
+                    )
+                    for _ in range(2)
+                ]
+            )
+        else:
+            self.extra_extractors = None
+
+    def forward(self, x, c, cls, blocks, deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks):
+        x = self.injector(
+            query=x,
+            reference_points=deform_inputs1[0],
+            feat=c,
+            spatial_shapes=deform_inputs1[1],
+            level_start_index=deform_inputs1[2],
+        )
+        x = torch.cat((cls, x), dim=1)
+        for idx, blk in enumerate(blocks):
+            x = blk(x, H_toks, W_toks)
+        cls, x = (
+            x[
+                :,
+                :1,
+            ],
+            x[
+                :,
+                1:,
+            ],
+        )
+        c = self.extractor(
+            query=c,
+            reference_points=deform_inputs2[0],
+            feat=x,
+            spatial_shapes=deform_inputs2[1],
+            level_start_index=deform_inputs2[2],
+            H=H_c,
+            W=W_c,
+        )
+        if self.extra_extractors is not None:
+            for extractor in self.extra_extractors:
+                c = extractor(
+                    query=c,
+                    reference_points=deform_inputs2[0],
+                    feat=x,
+                    spatial_shapes=deform_inputs2[1],
+                    level_start_index=deform_inputs2[2],
+                    H=H_c,
+                    W=W_c,
+                )
+        return x, c, cls
+
+
+class SpatialPriorModule(nn.Module):
+    def __init__(self, inplanes=64, embed_dim=384, with_cp=False):
+        super().__init__()
+        self.with_cp = with_cp
+
+        self.stem = nn.Sequential(
+            *[
+                nn.Conv2d(3, inplanes, kernel_size=3, stride=2, padding=1, bias=False),
+                nn.SyncBatchNorm(inplanes),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1, bias=False),
+                nn.SyncBatchNorm(inplanes),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1, bias=False),
+                nn.SyncBatchNorm(inplanes),
+                nn.ReLU(inplace=True),
+                nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
+            ]
+        )
+        self.conv2 = nn.Sequential(
+            *[
+                nn.Conv2d(inplanes, 2 * inplanes, kernel_size=3, stride=2, padding=1, bias=False),
+                nn.SyncBatchNorm(2 * inplanes),
+                nn.ReLU(inplace=True),
+            ]
+        )
+        self.conv3 = nn.Sequential(
+            *[
+                nn.Conv2d(2 * inplanes, 4 * inplanes, kernel_size=3, stride=2, padding=1, bias=False),
+                nn.SyncBatchNorm(4 * inplanes),
+                nn.ReLU(inplace=True),
+            ]
+        )
+        self.conv4 = nn.Sequential(
+            *[
+                nn.Conv2d(4 * inplanes, 4 * inplanes, kernel_size=3, stride=2, padding=1, bias=False),
+                nn.SyncBatchNorm(4 * inplanes),
+                nn.ReLU(inplace=True),
+            ]
+        )
+        self.fc1 = nn.Conv2d(inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True)
+        self.fc2 = nn.Conv2d(2 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True)
+        self.fc3 = nn.Conv2d(4 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True)
+        self.fc4 = nn.Conv2d(4 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True)
+
+    def forward(self, x):
+        def _inner_forward(x):
+            c1 = self.stem(x)
+            c2 = self.conv2(c1)
+            c3 = self.conv3(c2)
+            c4 = self.conv4(c3)
+            c1 = self.fc1(c1)
+            c2 = self.fc2(c2)
+            c3 = self.fc3(c3)
+            c4 = self.fc4(c4)
+
+            bs, dim, _, _ = c1.shape
+            # c1 = c1.view(bs, dim, -1).transpose(1, 2)  # 4s
+            c2 = c2.view(bs, dim, -1).transpose(1, 2)  # 8s
+            c3 = c3.view(bs, dim, -1).transpose(1, 2)  # 16s
+            c4 = c4.view(bs, dim, -1).transpose(1, 2)  # 32s
+
+            return c1, c2, c3, c4
+
+        if self.with_cp and x.requires_grad:
+            outs = cp.checkpoint(_inner_forward, x)
+        else:
+            outs = _inner_forward(x)
+        return outs
diff --git a/src/dinov2/eval/segmentation_m2f/models/backbones/drop_path.py b/src/dinov2/eval/segmentation_m2f/models/backbones/drop_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..864eb8738c44652d12b979fc811503f21cbb00dd
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/backbones/drop_path.py
@@ -0,0 +1,32 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
+
+from torch import nn
+
+
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob: float = 0.0):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
diff --git a/src/dinov2/eval/segmentation_m2f/models/backbones/vit.py b/src/dinov2/eval/segmentation_m2f/models/backbones/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a147570451bd2fbd016ddfafbbfa33035cbd4f8
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/backbones/vit.py
@@ -0,0 +1,552 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+"""Vision Transformer (ViT) in PyTorch.
+
+A PyTorch implement of Vision Transformers as described in:
+
+'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
+    - https://arxiv.org/abs/2010.11929
+
+`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
+    - https://arxiv.org/abs/2106.10270
+
+The official jax code is released and available at https://github.com/google-research/vision_transformer
+
+DeiT model defs and weights from https://github.com/facebookresearch/deit,
+paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
+
+Acknowledgments:
+* The paper authors for releasing code and weights, thanks!
+* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
+for some einops/einsum fun
+* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
+* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+import logging
+import math
+from functools import partial
+from itertools import repeat
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+from mmcv.runner import BaseModule, load_checkpoint
+from mmseg.ops import resize
+from mmseg.utils import get_root_logger
+from torch import Tensor
+
+from .drop_path import DropPath
+
+
+def to_2tuple(x):
+    return tuple(repeat(x, 2))
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        swiglu_hidden_features = int(2 * hidden_features / 3)
+        align_as = 8
+        swiglu_hidden_features = (swiglu_hidden_features + align_as - 1) // align_as * align_as
+        self.w1 = nn.Linear(in_features, swiglu_hidden_features)
+        self.w2 = nn.Linear(in_features, swiglu_hidden_features)
+        self.w3 = nn.Linear(swiglu_hidden_features, out_features)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x1 = self.w1(x)
+        x2 = self.w2(x)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+
+
+class PatchEmbed(nn.Module):
+    """2D Image to Patch Embedding."""
+
+    def __init__(
+        self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, bias=True
+    ):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
+        self.num_patches = self.grid_size[0] * self.grid_size[1]
+        self.flatten = flatten
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        x = self.proj(x)
+        _, _, H, W = x.shape
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        x = self.norm(x)
+        return x, H, W
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, H, W):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: Tensor, H, W) -> Tensor:
+        from xformers.ops import memory_efficient_attention, unbind
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowedAttention(nn.Module):
+    def __init__(
+        self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0, window_size=14, pad_mode="constant"
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.window_size = window_size
+        self.pad_mode = pad_mode
+
+    def forward(self, x, H, W):
+        B, N, C = x.shape
+        N_ = self.window_size * self.window_size
+        H_ = math.ceil(H / self.window_size) * self.window_size
+        W_ = math.ceil(W / self.window_size) * self.window_size
+
+        qkv = self.qkv(x)  # [B, N, C]
+        qkv = qkv.transpose(1, 2).reshape(B, C * 3, H, W)  # [B, C, H, W]
+        qkv = F.pad(qkv, [0, W_ - W, 0, H_ - H], mode=self.pad_mode)
+
+        qkv = F.unfold(
+            qkv, kernel_size=(self.window_size, self.window_size), stride=(self.window_size, self.window_size)
+        )
+        B, C_kw_kw, L = qkv.shape  # L - the num of windows
+        qkv = qkv.reshape(B, C * 3, N_, L).permute(0, 3, 2, 1)  # [B, L, N_, C]
+        qkv = qkv.reshape(B, L, N_, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        # q,k,v [B, L, num_head, N_, C/num_head]
+        attn = (q @ k.transpose(-2, -1)) * self.scale  # [B, L, num_head, N_, N_]
+        # if self.mask:
+        #     attn = attn * mask
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)  # [B, L, num_head, N_, N_]
+        # attn @ v = [B, L, num_head, N_, C/num_head]
+        x = (attn @ v).permute(0, 2, 4, 3, 1).reshape(B, C_kw_kw // 3, L)
+
+        x = F.fold(
+            x,
+            output_size=(H_, W_),
+            kernel_size=(self.window_size, self.window_size),
+            stride=(self.window_size, self.window_size),
+        )  # [B, C, H_, W_]
+        x = x[:, :, :H, :W].reshape(B, C, N).transpose(-1, -2)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+# class WindowedAttention(nn.Module):
+#     def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., window_size=14, pad_mode="constant"):
+#         super().__init__()
+#         self.num_heads = num_heads
+#         head_dim = dim // num_heads
+#         self.scale = head_dim ** -0.5
+#
+#         self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+#         self.attn_drop = nn.Dropout(attn_drop)
+#         self.proj = nn.Linear(dim, dim)
+#         self.proj_drop = nn.Dropout(proj_drop)
+#         self.window_size = window_size
+#         self.pad_mode = pad_mode
+#
+#     def forward(self, x, H, W):
+#         B, N, C = x.shape
+#
+#         N_ = self.window_size * self.window_size
+#         H_ = math.ceil(H / self.window_size) * self.window_size
+#         W_ = math.ceil(W / self.window_size) * self.window_size
+#         x = x.view(B, H, W, C)
+#         x = F.pad(x, [0, 0, 0, W_ - W, 0, H_- H], mode=self.pad_mode)
+#
+#         x = window_partition(x, window_size=self.window_size)# nW*B, window_size, window_size, C
+#         x = x.view(-1, N_, C)
+#
+#         qkv = self.qkv(x).view(-1, N_, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+#         q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
+#         attn = (q @ k.transpose(-2, -1)) * self.scale # [B, L, num_head, N_, N_]
+#         attn = attn.softmax(dim=-1)
+#         attn = self.attn_drop(attn) # [B, L, num_head, N_, N_]
+#         x = (attn @ v).transpose(1, 2).reshape(-1, self.window_size, self.window_size, C)
+#
+#         x = window_reverse(x, self.window_size, H_, W_)
+#         x = x[:, :H, :W, :].reshape(B, N, C).contiguous()
+#         x = self.proj(x)
+#         x = self.proj_drop(x)
+#         return x
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        mlp_ratio=4.0,
+        qkv_bias=False,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+        windowed=False,
+        window_size=14,
+        pad_mode="constant",
+        layer_scale=False,
+        with_cp=False,
+        ffn_layer=Mlp,
+        memeff=False,
+    ):
+        super().__init__()
+        self.with_cp = with_cp
+        self.norm1 = norm_layer(dim)
+        if windowed:
+            self.attn = WindowedAttention(
+                dim,
+                num_heads=num_heads,
+                qkv_bias=qkv_bias,
+                attn_drop=attn_drop,
+                proj_drop=drop,
+                window_size=window_size,
+                pad_mode=pad_mode,
+            )
+        elif memeff:
+            self.attn = MemEffAttention(
+                dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop
+            )
+        else:
+            self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        self.layer_scale = layer_scale
+        if layer_scale:
+            self.gamma1 = nn.Parameter(torch.ones((dim)), requires_grad=True)
+            self.gamma2 = nn.Parameter(torch.ones((dim)), requires_grad=True)
+
+    def forward(self, x, H, W):
+        def _inner_forward(x):
+            if self.layer_scale:
+                x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x), H, W))
+                x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))
+            else:
+                x = x + self.drop_path(self.attn(self.norm1(x), H, W))
+                x = x + self.drop_path(self.mlp(self.norm2(x)))
+            return x
+
+        if self.with_cp and x.requires_grad:
+            x = cp.checkpoint(_inner_forward, x)
+        else:
+            x = _inner_forward(x)
+
+        return x
+
+
+class TIMMVisionTransformer(BaseModule):
+    """Vision Transformer.
+
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
+        - https://arxiv.org/abs/2010.11929
+
+    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
+        - https://arxiv.org/abs/2012.12877
+    """
+
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        num_classes=1000,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        layer_scale=True,
+        embed_layer=PatchEmbed,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        act_layer=nn.GELU,
+        window_attn=False,
+        window_size=14,
+        pretrained=None,
+        with_cp=False,
+        pre_norm=False,
+        ffn_type="mlp",
+        memeff=False,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            num_classes (int): number of classes for classification head
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            drop_rate (float): dropout rate
+            attn_drop_rate (float): attention dropout rate
+            drop_path_rate (float): stochastic depth rate
+            embed_layer (nn.Module): patch embedding layer
+            norm_layer: (nn.Module): normalization layer
+            pretrained: (str): pretrained path
+        """
+        super().__init__()
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+        act_layer = act_layer or nn.GELU
+        self.norm_layer = norm_layer
+        self.act_layer = act_layer
+        self.pretrain_size = img_size
+        self.drop_path_rate = drop_path_rate
+        self.drop_rate = drop_rate
+        self.patch_size = patch_size
+
+        window_attn = [window_attn] * depth if not isinstance(window_attn, list) else window_attn
+        window_size = [window_size] * depth if not isinstance(window_size, list) else window_size
+        logging.info("window attention:", window_attn)
+        logging.info("window size:", window_size)
+        logging.info("layer scale:", layer_scale)
+
+        self.patch_embed = embed_layer(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, bias=not pre_norm
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        ffn_types = {"mlp": Mlp, "swiglu": SwiGLUFFN}
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.Sequential(
+            *[
+                Block(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    drop=drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    act_layer=act_layer,
+                    windowed=window_attn[i],
+                    window_size=window_size[i],
+                    layer_scale=layer_scale,
+                    with_cp=with_cp,
+                    ffn_layer=ffn_types[ffn_type],
+                    memeff=memeff,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # self.norm = norm_layer(embed_dim)
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        # For CLIP
+        if pre_norm:
+            norm_pre = norm_layer(embed_dim)
+            self.norm_pre = norm_pre
+        else:
+            self.norm_pre = nn.Identity()
+        self.init_weights(pretrained)
+
+    def init_weights(self, pretrained=None):
+        if isinstance(pretrained, str):
+            logger = get_root_logger()
+            load_checkpoint(self, pretrained, map_location="cpu", strict=False, logger=logger)
+
+    def forward_features(self, x):
+        x, H, W = self.patch_embed(x)
+        cls_token = self.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_token, x), dim=1)
+        x = self.pos_drop(x + self.pos_embed)
+
+        # For CLIP
+        x = self.norm_pre(x)
+
+        for blk in self.blocks:
+            x = blk(x, H, W)
+        x = self.norm(x)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        return x
+
+    @staticmethod
+    def resize_pos_embed(pos_embed, input_shpae, pos_shape, mode):
+        """Resize pos_embed weights.
+
+        Resize pos_embed using bicubic interpolate method.
+        Args:
+            pos_embed (torch.Tensor): Position embedding weights.
+            input_shpae (tuple): Tuple for (downsampled input image height,
+                downsampled input image width).
+            pos_shape (tuple): The resolution of downsampled origin training
+                image.
+            mode (str): Algorithm used for upsampling:
+                ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |
+                ``'trilinear'``. Default: ``'nearest'``
+        Return:
+            torch.Tensor: The resized pos_embed of shape [B, L_new, C]
+        """
+        assert pos_embed.ndim == 3, "shape of pos_embed must be [B, L, C]"
+        pos_h, pos_w = pos_shape
+        # keep dim for easy deployment
+        cls_token_weight = pos_embed[:, 0:1]
+        pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w) :]
+        pos_embed_weight = pos_embed_weight.reshape(1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)
+        pos_embed_weight = resize(pos_embed_weight, size=input_shpae, align_corners=False, mode=mode)
+        pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)
+        pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)
+        return pos_embed
diff --git a/src/dinov2/eval/segmentation_m2f/models/backbones/vit_adapter.py b/src/dinov2/eval/segmentation_m2f/models/backbones/vit_adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebc4f0f65e04ed764464d141607b3b2073220f6b
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/backbones/vit_adapter.py
@@ -0,0 +1,217 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmseg.models.builder import BACKBONES
+from torch.nn.init import normal_
+
+from ...ops.modules import MSDeformAttn
+from .adapter_modules import InteractionBlock, InteractionBlockWithCls, SpatialPriorModule, deform_inputs
+from .vit import TIMMVisionTransformer
+
+
+@BACKBONES.register_module()
+class ViTAdapter(TIMMVisionTransformer):
+    def __init__(
+        self,
+        pretrain_size=224,
+        num_heads=12,
+        conv_inplane=64,
+        n_points=4,
+        deform_num_heads=6,
+        init_values=0.0,
+        interaction_indexes=None,
+        with_cffn=True,
+        cffn_ratio=0.25,
+        deform_ratio=1.0,
+        add_vit_feature=True,
+        pretrained=None,
+        use_extra_extractor=True,
+        freeze_vit=False,
+        use_cls=True,
+        with_cp=False,
+        *args,
+        **kwargs
+    ):
+
+        super().__init__(num_heads=num_heads, pretrained=pretrained, with_cp=with_cp, *args, **kwargs)
+        if freeze_vit:
+            for param in self.parameters():
+                param.requires_grad = False
+
+        # self.num_classes = 80
+        self.use_cls = use_cls
+        if not self.use_cls:
+            self.cls_token = None
+        self.num_block = len(self.blocks)
+        self.pretrain_size = (pretrain_size, pretrain_size)
+        self.interaction_indexes = interaction_indexes
+        self.add_vit_feature = add_vit_feature
+        embed_dim = self.embed_dim
+
+        block_fn = InteractionBlockWithCls if use_cls else InteractionBlock
+
+        self.level_embed = nn.Parameter(torch.zeros(3, embed_dim))
+        self.spm = SpatialPriorModule(inplanes=conv_inplane, embed_dim=embed_dim, with_cp=False)
+        self.interactions = nn.Sequential(
+            *[
+                block_fn(
+                    dim=embed_dim,
+                    num_heads=deform_num_heads,
+                    n_points=n_points,
+                    init_values=init_values,
+                    drop_path=self.drop_path_rate,
+                    norm_layer=self.norm_layer,
+                    with_cffn=with_cffn,
+                    cffn_ratio=cffn_ratio,
+                    deform_ratio=deform_ratio,
+                    extra_extractor=((True if i == len(interaction_indexes) - 1 else False) and use_extra_extractor),
+                    with_cp=with_cp,
+                )
+                for i in range(len(interaction_indexes))
+            ]
+        )
+        self.up = nn.ConvTranspose2d(embed_dim, embed_dim, 2, 2)
+        self.norm1 = nn.SyncBatchNorm(embed_dim)
+        self.norm2 = nn.SyncBatchNorm(embed_dim)
+        self.norm3 = nn.SyncBatchNorm(embed_dim)
+        self.norm4 = nn.SyncBatchNorm(embed_dim)
+
+        self.up.apply(self._init_weights)
+        self.spm.apply(self._init_weights)
+        self.interactions.apply(self._init_weights)
+        self.apply(self._init_deform_weights)
+        normal_(self.level_embed)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            torch.nn.init.trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm2d):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+        elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+
+    def _get_pos_embed(self, pos_embed, H, W):
+        pos_embed = pos_embed.reshape(
+            1, self.pretrain_size[0] // self.patch_size, self.pretrain_size[1] // self.patch_size, -1
+        ).permute(0, 3, 1, 2)
+        pos_embed = (
+            F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False)
+            .reshape(1, -1, H * W)
+            .permute(0, 2, 1)
+        )
+        return pos_embed
+
+    def _init_deform_weights(self, m):
+        if isinstance(m, MSDeformAttn):
+            m._reset_parameters()
+
+    def _add_level_embed(self, c2, c3, c4):
+        c2 = c2 + self.level_embed[0]
+        c3 = c3 + self.level_embed[1]
+        c4 = c4 + self.level_embed[2]
+        return c2, c3, c4
+
+    def forward(self, x):
+        deform_inputs1, deform_inputs2 = deform_inputs(x, self.patch_size)
+
+        # SPM forward
+        c1, c2, c3, c4 = self.spm(x)
+        c2, c3, c4 = self._add_level_embed(c2, c3, c4)
+        c = torch.cat([c2, c3, c4], dim=1)
+
+        # Patch Embedding forward
+        H_c, W_c = x.shape[2] // 16, x.shape[3] // 16
+        x, H_toks, W_toks = self.patch_embed(x)
+        # print("H_toks, W_toks =", H_toks, W_toks)
+        bs, n, dim = x.shape
+        pos_embed = self._get_pos_embed(self.pos_embed[:, 1:], H_toks, W_toks)
+        if self.use_cls:
+            cls_token = self.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+            x = torch.cat((cls_token, x), dim=1)
+            pos_embed = torch.cat((self.pos_embed[:, :1], pos_embed), dim=1)
+        x = self.pos_drop(x + pos_embed)
+        # For CLIP
+        x = self.norm_pre(x)
+
+        # Interaction
+        if self.use_cls:
+            cls, x = (
+                x[
+                    :,
+                    :1,
+                ],
+                x[
+                    :,
+                    1:,
+                ],
+            )
+        outs = list()
+        for i, layer in enumerate(self.interactions):
+            indexes = self.interaction_indexes[i]
+            if self.use_cls:
+                x, c, cls = layer(
+                    x,
+                    c,
+                    cls,
+                    self.blocks[indexes[0] : indexes[-1] + 1],
+                    deform_inputs1,
+                    deform_inputs2,
+                    H_c,
+                    W_c,
+                    H_toks,
+                    W_toks,
+                )
+            else:
+                x, c = layer(
+                    x,
+                    c,
+                    self.blocks[indexes[0] : indexes[-1] + 1],
+                    deform_inputs1,
+                    deform_inputs2,
+                    H_c,
+                    W_c,
+                    H_toks,
+                    W_toks,
+                )
+            outs.append(x.transpose(1, 2).view(bs, dim, H_toks, W_toks).contiguous())
+
+        # Split & Reshape
+        c2 = c[:, 0 : c2.size(1), :]
+        c3 = c[:, c2.size(1) : c2.size(1) + c3.size(1), :]
+        c4 = c[:, c2.size(1) + c3.size(1) :, :]
+
+        c2 = c2.transpose(1, 2).view(bs, dim, H_c * 2, W_c * 2).contiguous()
+        c3 = c3.transpose(1, 2).view(bs, dim, H_c, W_c).contiguous()
+        c4 = c4.transpose(1, 2).view(bs, dim, H_c // 2, W_c // 2).contiguous()
+        c1 = self.up(c2) + c1
+
+        if self.add_vit_feature:
+            x1, x2, x3, x4 = outs
+
+            x1 = F.interpolate(x1, size=(4 * H_c, 4 * W_c), mode="bilinear", align_corners=False)
+            x2 = F.interpolate(x2, size=(2 * H_c, 2 * W_c), mode="bilinear", align_corners=False)
+            x3 = F.interpolate(x3, size=(1 * H_c, 1 * W_c), mode="bilinear", align_corners=False)
+            x4 = F.interpolate(x4, size=(H_c // 2, W_c // 2), mode="bilinear", align_corners=False)
+            # print(c1.shape, c2.shape, c3.shape, c4.shape, x1.shape, x2.shape, x3.shape, x4.shape, H_c, H_toks)
+            c1, c2, c3, c4 = c1 + x1, c2 + x2, c3 + x3, c4 + x4
+
+        # Final Norm
+        f1 = self.norm1(c1)
+        f2 = self.norm2(c2)
+        f3 = self.norm3(c3)
+        f4 = self.norm4(c4)
+        return [f1, f2, f3, f4]
diff --git a/src/dinov2/eval/segmentation_m2f/models/builder.py b/src/dinov2/eval/segmentation_m2f/models/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7cf7b919f6b0e8e00bde45bc244d9c29a36fed6
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/builder.py
@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from mmcv.utils import Registry
+
+TRANSFORMER = Registry("Transformer")
+MASK_ASSIGNERS = Registry("mask_assigner")
+MATCH_COST = Registry("match_cost")
+
+
+def build_match_cost(cfg):
+    """Build Match Cost."""
+    return MATCH_COST.build(cfg)
+
+
+def build_assigner(cfg):
+    """Build Assigner."""
+    return MASK_ASSIGNERS.build(cfg)
+
+
+def build_transformer(cfg):
+    """Build Transformer."""
+    return TRANSFORMER.build(cfg)
diff --git a/src/dinov2/eval/segmentation_m2f/models/decode_heads/__init__.py b/src/dinov2/eval/segmentation_m2f/models/decode_heads/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..01f08b88950750337781fc671adfea2a935ea8fe
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/decode_heads/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .mask2former_head import Mask2FormerHead
diff --git a/src/dinov2/eval/segmentation_m2f/models/decode_heads/mask2former_head.py b/src/dinov2/eval/segmentation_m2f/models/decode_heads/mask2former_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1705fc444fa8d1583d88fca36d7fe1e060db9e7
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/decode_heads/mask2former_head.py
@@ -0,0 +1,544 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import copy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import Conv2d, build_plugin_layer, caffe2_xavier_init
+from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer_sequence
+from mmcv.ops import point_sample
+from mmcv.runner import ModuleList, force_fp32
+from mmseg.models.builder import HEADS, build_loss
+from mmseg.models.decode_heads.decode_head import BaseDecodeHead
+
+from ...core import build_sampler, multi_apply, reduce_mean
+from ..builder import build_assigner
+from ..utils import get_uncertain_point_coords_with_randomness
+
+
+@HEADS.register_module()
+class Mask2FormerHead(BaseDecodeHead):
+    """Implements the Mask2Former head.
+
+    See `Masked-attention Mask Transformer for Universal Image
+    Segmentation <https://arxiv.org/pdf/2112.01527>`_ for details.
+
+    Args:
+        in_channels (list[int]): Number of channels in the input feature map.
+        feat_channels (int): Number of channels for features.
+        out_channels (int): Number of channels for output.
+        num_things_classes (int): Number of things.
+        num_stuff_classes (int): Number of stuff.
+        num_queries (int): Number of query in Transformer decoder.
+        pixel_decoder (:obj:`mmcv.ConfigDict` | dict): Config for pixel
+            decoder. Defaults to None.
+        enforce_decoder_input_project (bool, optional): Whether to add
+            a layer to change the embed_dim of tranformer encoder in
+            pixel decoder to the embed_dim of transformer decoder.
+            Defaults to False.
+        transformer_decoder (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer decoder. Defaults to None.
+        positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer decoder position encoding. Defaults to None.
+        loss_cls (:obj:`mmcv.ConfigDict` | dict): Config of the classification
+            loss. Defaults to None.
+        loss_mask (:obj:`mmcv.ConfigDict` | dict): Config of the mask loss.
+            Defaults to None.
+        loss_dice (:obj:`mmcv.ConfigDict` | dict): Config of the dice loss.
+            Defaults to None.
+        train_cfg (:obj:`mmcv.ConfigDict` | dict): Training config of
+            Mask2Former head.
+        test_cfg (:obj:`mmcv.ConfigDict` | dict): Testing config of
+            Mask2Former head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Defaults to None.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        feat_channels,
+        out_channels,
+        num_things_classes=80,
+        num_stuff_classes=53,
+        num_queries=100,
+        num_transformer_feat_level=3,
+        pixel_decoder=None,
+        enforce_decoder_input_project=False,
+        transformer_decoder=None,
+        positional_encoding=None,
+        loss_cls=None,
+        loss_mask=None,
+        loss_dice=None,
+        train_cfg=None,
+        test_cfg=None,
+        init_cfg=None,
+        **kwargs,
+    ):
+        super(Mask2FormerHead, self).__init__(
+            in_channels=in_channels,
+            channels=feat_channels,
+            num_classes=(num_things_classes + num_stuff_classes),
+            init_cfg=init_cfg,
+            input_transform="multiple_select",
+            **kwargs,
+        )
+        self.num_things_classes = num_things_classes
+        self.num_stuff_classes = num_stuff_classes
+        self.num_classes = self.num_things_classes + self.num_stuff_classes
+        self.num_queries = num_queries
+        self.num_transformer_feat_level = num_transformer_feat_level
+        self.num_heads = transformer_decoder.transformerlayers.attn_cfgs.num_heads
+        self.num_transformer_decoder_layers = transformer_decoder.num_layers
+        assert pixel_decoder.encoder.transformerlayers.attn_cfgs.num_levels == num_transformer_feat_level
+        pixel_decoder_ = copy.deepcopy(pixel_decoder)
+        pixel_decoder_.update(in_channels=in_channels, feat_channels=feat_channels, out_channels=out_channels)
+        self.pixel_decoder = build_plugin_layer(pixel_decoder_)[1]
+        self.transformer_decoder = build_transformer_layer_sequence(transformer_decoder)
+        self.decoder_embed_dims = self.transformer_decoder.embed_dims
+
+        self.decoder_input_projs = ModuleList()
+        # from low resolution to high resolution
+        for _ in range(num_transformer_feat_level):
+            if self.decoder_embed_dims != feat_channels or enforce_decoder_input_project:
+                self.decoder_input_projs.append(Conv2d(feat_channels, self.decoder_embed_dims, kernel_size=1))
+            else:
+                self.decoder_input_projs.append(nn.Identity())
+        self.decoder_positional_encoding = build_positional_encoding(positional_encoding)
+        self.query_embed = nn.Embedding(self.num_queries, feat_channels)
+        self.query_feat = nn.Embedding(self.num_queries, feat_channels)
+        # from low resolution to high resolution
+        self.level_embed = nn.Embedding(self.num_transformer_feat_level, feat_channels)
+
+        self.cls_embed = nn.Linear(feat_channels, self.num_classes + 1)
+        self.mask_embed = nn.Sequential(
+            nn.Linear(feat_channels, feat_channels),
+            nn.ReLU(inplace=True),
+            nn.Linear(feat_channels, feat_channels),
+            nn.ReLU(inplace=True),
+            nn.Linear(feat_channels, out_channels),
+        )
+        self.conv_seg = None  # fix a bug here (conv_seg is not used)
+
+        self.test_cfg = test_cfg
+        self.train_cfg = train_cfg
+        if train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            self.sampler = build_sampler(self.train_cfg.sampler, context=self)
+            self.num_points = self.train_cfg.get("num_points", 12544)
+            self.oversample_ratio = self.train_cfg.get("oversample_ratio", 3.0)
+            self.importance_sample_ratio = self.train_cfg.get("importance_sample_ratio", 0.75)
+
+        self.class_weight = loss_cls.class_weight
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_mask = build_loss(loss_mask)
+        self.loss_dice = build_loss(loss_dice)
+
+    def init_weights(self):
+        for m in self.decoder_input_projs:
+            if isinstance(m, Conv2d):
+                caffe2_xavier_init(m, bias=0)
+
+        self.pixel_decoder.init_weights()
+
+        for p in self.transformer_decoder.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_normal_(p)
+
+    def get_targets(self, cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas):
+        """Compute classification and mask targets for all images for a decoder
+        layer.
+
+        Args:
+            cls_scores_list (list[Tensor]): Mask score logits from a single
+                decoder layer for all images. Each with shape [num_queries,
+                cls_out_channels].
+            mask_preds_list (list[Tensor]): Mask logits from a single decoder
+                layer for all images. Each with shape [num_queries, h, w].
+            gt_labels_list (list[Tensor]): Ground truth class indices for all
+                images. Each with shape (n, ), n is the sum of number of stuff
+                type and number of instance in a image.
+            gt_masks_list (list[Tensor]): Ground truth mask for each image,
+                each with shape (n, h, w).
+            img_metas (list[dict]): List of image meta information.
+
+        Returns:
+            tuple[list[Tensor]]: a tuple containing the following targets.
+
+                - labels_list (list[Tensor]): Labels of all images.
+                    Each with shape [num_queries, ].
+                - label_weights_list (list[Tensor]): Label weights of all
+                    images.Each with shape [num_queries, ].
+                - mask_targets_list (list[Tensor]): Mask targets of all images.
+                    Each with shape [num_queries, h, w].
+                - mask_weights_list (list[Tensor]): Mask weights of all images.
+                    Each with shape [num_queries, ].
+                - num_total_pos (int): Number of positive samples in all
+                    images.
+                - num_total_neg (int): Number of negative samples in all
+                    images.
+        """
+        (
+            labels_list,
+            label_weights_list,
+            mask_targets_list,
+            mask_weights_list,
+            pos_inds_list,
+            neg_inds_list,
+        ) = multi_apply(
+            self._get_target_single, cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas
+        )
+
+        num_total_pos = sum((inds.numel() for inds in pos_inds_list))
+        num_total_neg = sum((inds.numel() for inds in neg_inds_list))
+        return (labels_list, label_weights_list, mask_targets_list, mask_weights_list, num_total_pos, num_total_neg)
+
+    def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks, img_metas):
+        """Compute classification and mask targets for one image.
+
+        Args:
+            cls_score (Tensor): Mask score logits from a single decoder layer
+                for one image. Shape (num_queries, cls_out_channels).
+            mask_pred (Tensor): Mask logits for a single decoder layer for one
+                image. Shape (num_queries, h, w).
+            gt_labels (Tensor): Ground truth class indices for one image with
+                shape (num_gts, ).
+            gt_masks (Tensor): Ground truth mask for each image, each with
+                shape (num_gts, h, w).
+            img_metas (dict): Image informtation.
+
+        Returns:
+            tuple[Tensor]: A tuple containing the following for one image.
+
+                - labels (Tensor): Labels of each image. \
+                    shape (num_queries, ).
+                - label_weights (Tensor): Label weights of each image. \
+                    shape (num_queries, ).
+                - mask_targets (Tensor): Mask targets of each image. \
+                    shape (num_queries, h, w).
+                - mask_weights (Tensor): Mask weights of each image. \
+                    shape (num_queries, ).
+                - pos_inds (Tensor): Sampled positive indices for each \
+                    image.
+                - neg_inds (Tensor): Sampled negative indices for each \
+                    image.
+        """
+        # sample points
+        num_queries = cls_score.shape[0]
+        num_gts = gt_labels.shape[0]
+
+        point_coords = torch.rand((1, self.num_points, 2), device=cls_score.device)
+        # shape (num_queries, num_points)
+        mask_points_pred = point_sample(mask_pred.unsqueeze(1), point_coords.repeat(num_queries, 1, 1)).squeeze(1)
+        # shape (num_gts, num_points)
+        gt_points_masks = point_sample(gt_masks.unsqueeze(1).float(), point_coords.repeat(num_gts, 1, 1)).squeeze(1)
+
+        # assign and sample
+        assign_result = self.assigner.assign(cls_score, mask_points_pred, gt_labels, gt_points_masks, img_metas)
+        sampling_result = self.sampler.sample(assign_result, mask_pred, gt_masks)
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+
+        # label target
+        labels = gt_labels.new_full((self.num_queries,), self.num_classes, dtype=torch.long)
+        labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
+        label_weights = gt_labels.new_ones((self.num_queries,))
+
+        # mask target
+        mask_targets = gt_masks[sampling_result.pos_assigned_gt_inds]
+        mask_weights = mask_pred.new_zeros((self.num_queries,))
+        mask_weights[pos_inds] = 1.0
+
+        return (labels, label_weights, mask_targets, mask_weights, pos_inds, neg_inds)
+
+    def loss_single(self, cls_scores, mask_preds, gt_labels_list, gt_masks_list, img_metas):
+        """Loss function for outputs from a single decoder layer.
+
+        Args:
+            cls_scores (Tensor): Mask score logits from a single decoder layer
+                for all images. Shape (batch_size, num_queries,
+                cls_out_channels). Note `cls_out_channels` should includes
+                background.
+            mask_preds (Tensor): Mask logits for a pixel decoder for all
+                images. Shape (batch_size, num_queries, h, w).
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image, each with shape (num_gts, ).
+            gt_masks_list (list[Tensor]): Ground truth mask for each image,
+                each with shape (num_gts, h, w).
+            img_metas (list[dict]): List of image meta information.
+
+        Returns:
+            tuple[Tensor]: Loss components for outputs from a single \
+                decoder layer.
+        """
+        num_imgs = cls_scores.size(0)
+        cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
+        mask_preds_list = [mask_preds[i] for i in range(num_imgs)]
+        (
+            labels_list,
+            label_weights_list,
+            mask_targets_list,
+            mask_weights_list,
+            num_total_pos,
+            num_total_neg,
+        ) = self.get_targets(cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas)
+        # shape (batch_size, num_queries)
+        labels = torch.stack(labels_list, dim=0)
+        # shape (batch_size, num_queries)
+        label_weights = torch.stack(label_weights_list, dim=0)
+        # shape (num_total_gts, h, w)
+        mask_targets = torch.cat(mask_targets_list, dim=0)
+        # shape (batch_size, num_queries)
+        mask_weights = torch.stack(mask_weights_list, dim=0)
+
+        # classfication loss
+        # shape (batch_size * num_queries, )
+        cls_scores = cls_scores.flatten(0, 1)
+        labels = labels.flatten(0, 1)
+        label_weights = label_weights.flatten(0, 1)
+
+        class_weight = cls_scores.new_tensor(self.class_weight)
+        loss_cls = self.loss_cls(cls_scores, labels, label_weights, avg_factor=class_weight[labels].sum())
+
+        num_total_masks = reduce_mean(cls_scores.new_tensor([num_total_pos]))
+        num_total_masks = max(num_total_masks, 1)
+
+        # extract positive ones
+        # shape (batch_size, num_queries, h, w) -> (num_total_gts, h, w)
+        mask_preds = mask_preds[mask_weights > 0]
+
+        if mask_targets.shape[0] == 0:
+            # zero match
+            loss_dice = mask_preds.sum()
+            loss_mask = mask_preds.sum()
+            return loss_cls, loss_mask, loss_dice
+
+        with torch.no_grad():
+            points_coords = get_uncertain_point_coords_with_randomness(
+                mask_preds.unsqueeze(1), None, self.num_points, self.oversample_ratio, self.importance_sample_ratio
+            )
+            # shape (num_total_gts, h, w) -> (num_total_gts, num_points)
+            mask_point_targets = point_sample(mask_targets.unsqueeze(1).float(), points_coords).squeeze(1)
+        # shape (num_queries, h, w) -> (num_queries, num_points)
+        mask_point_preds = point_sample(mask_preds.unsqueeze(1), points_coords).squeeze(1)
+
+        # dice loss
+        loss_dice = self.loss_dice(mask_point_preds, mask_point_targets, avg_factor=num_total_masks)
+
+        # mask loss
+        # shape (num_queries, num_points) -> (num_queries * num_points, )
+        mask_point_preds = mask_point_preds.reshape(-1, 1)
+        # shape (num_total_gts, num_points) -> (num_total_gts * num_points, )
+        mask_point_targets = mask_point_targets.reshape(-1)
+        loss_mask = self.loss_mask(mask_point_preds, mask_point_targets, avg_factor=num_total_masks * self.num_points)
+
+        return loss_cls, loss_mask, loss_dice
+
+    @force_fp32(apply_to=("all_cls_scores", "all_mask_preds"))
+    def loss(self, all_cls_scores, all_mask_preds, gt_labels_list, gt_masks_list, img_metas):
+        """Loss function.
+
+        Args:
+            all_cls_scores (Tensor): Classification scores for all decoder
+                layers with shape [num_decoder, batch_size, num_queries,
+                cls_out_channels].
+            all_mask_preds (Tensor): Mask scores for all decoder layers with
+                shape [num_decoder, batch_size, num_queries, h, w].
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (n, ). n is the sum of number of stuff type
+                and number of instance in a image.
+            gt_masks_list (list[Tensor]): Ground truth mask for each image with
+                shape (n, h, w).
+            img_metas (list[dict]): List of image meta information.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        num_dec_layers = len(all_cls_scores)
+        all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
+        all_gt_masks_list = [gt_masks_list for _ in range(num_dec_layers)]
+        img_metas_list = [img_metas for _ in range(num_dec_layers)]
+        losses_cls, losses_mask, losses_dice = multi_apply(
+            self.loss_single, all_cls_scores, all_mask_preds, all_gt_labels_list, all_gt_masks_list, img_metas_list
+        )
+
+        loss_dict = dict()
+        # loss from the last decoder layer
+        loss_dict["loss_cls"] = losses_cls[-1]
+        loss_dict["loss_mask"] = losses_mask[-1]
+        loss_dict["loss_dice"] = losses_dice[-1]
+        # loss from other decoder layers
+        num_dec_layer = 0
+        for loss_cls_i, loss_mask_i, loss_dice_i in zip(losses_cls[:-1], losses_mask[:-1], losses_dice[:-1]):
+            loss_dict[f"d{num_dec_layer}.loss_cls"] = loss_cls_i
+            loss_dict[f"d{num_dec_layer}.loss_mask"] = loss_mask_i
+            loss_dict[f"d{num_dec_layer}.loss_dice"] = loss_dice_i
+            num_dec_layer += 1
+        return loss_dict
+
+    def forward_head(self, decoder_out, mask_feature, attn_mask_target_size):
+        """Forward for head part which is called after every decoder layer.
+
+        Args:
+            decoder_out (Tensor): in shape (num_queries, batch_size, c).
+            mask_feature (Tensor): in shape (batch_size, c, h, w).
+            attn_mask_target_size (tuple[int, int]): target attention
+                mask size.
+
+        Returns:
+            tuple: A tuple contain three elements.
+
+            - cls_pred (Tensor): Classification scores in shape \
+                (batch_size, num_queries, cls_out_channels). \
+                Note `cls_out_channels` should includes background.
+            - mask_pred (Tensor): Mask scores in shape \
+                (batch_size, num_queries,h, w).
+            - attn_mask (Tensor): Attention mask in shape \
+                (batch_size * num_heads, num_queries, h, w).
+        """
+        decoder_out = self.transformer_decoder.post_norm(decoder_out)
+        decoder_out = decoder_out.transpose(0, 1)
+        # shape (num_queries, batch_size, c)
+        cls_pred = self.cls_embed(decoder_out)
+        # shape (num_queries, batch_size, c)
+        mask_embed = self.mask_embed(decoder_out)
+        # shape (num_queries, batch_size, h, w)
+        mask_pred = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_feature)
+        attn_mask = F.interpolate(mask_pred, attn_mask_target_size, mode="bilinear", align_corners=False)
+        # shape (num_queries, batch_size, h, w) ->
+        #   (batch_size * num_head, num_queries, h, w)
+        attn_mask = attn_mask.flatten(2).unsqueeze(1).repeat((1, self.num_heads, 1, 1)).flatten(0, 1)
+        attn_mask = attn_mask.sigmoid() < 0.5
+        attn_mask = attn_mask.detach()
+
+        return cls_pred, mask_pred, attn_mask
+
+    def forward(self, feats, img_metas):
+        """Forward function.
+
+        Args:
+            feats (list[Tensor]): Multi scale Features from the
+                upstream network, each is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple: A tuple contains two elements.
+
+            - cls_pred_list (list[Tensor)]: Classification logits \
+                for each decoder layer. Each is a 3D-tensor with shape \
+                (batch_size, num_queries, cls_out_channels). \
+                Note `cls_out_channels` should includes background.
+            - mask_pred_list (list[Tensor]): Mask logits for each \
+                decoder layer. Each with shape (batch_size, num_queries, \
+                 h, w).
+        """
+        batch_size = len(img_metas)
+        mask_features, multi_scale_memorys = self.pixel_decoder(feats)
+        # multi_scale_memorys (from low resolution to high resolution)
+        decoder_inputs = []
+        decoder_positional_encodings = []
+        for i in range(self.num_transformer_feat_level):
+            decoder_input = self.decoder_input_projs[i](multi_scale_memorys[i])
+            # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
+            decoder_input = decoder_input.flatten(2).permute(2, 0, 1)
+            level_embed = self.level_embed.weight[i].view(1, 1, -1)
+            decoder_input = decoder_input + level_embed
+            # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
+            mask = decoder_input.new_zeros((batch_size,) + multi_scale_memorys[i].shape[-2:], dtype=torch.bool)
+            decoder_positional_encoding = self.decoder_positional_encoding(mask)
+            decoder_positional_encoding = decoder_positional_encoding.flatten(2).permute(2, 0, 1)
+            decoder_inputs.append(decoder_input)
+            decoder_positional_encodings.append(decoder_positional_encoding)
+        # shape (num_queries, c) -> (num_queries, batch_size, c)
+        query_feat = self.query_feat.weight.unsqueeze(1).repeat((1, batch_size, 1))
+        query_embed = self.query_embed.weight.unsqueeze(1).repeat((1, batch_size, 1))
+
+        cls_pred_list = []
+        mask_pred_list = []
+        cls_pred, mask_pred, attn_mask = self.forward_head(query_feat, mask_features, multi_scale_memorys[0].shape[-2:])
+        cls_pred_list.append(cls_pred)
+        mask_pred_list.append(mask_pred)
+
+        for i in range(self.num_transformer_decoder_layers):
+            level_idx = i % self.num_transformer_feat_level
+            # if a mask is all True(all background), then set it all False.
+            attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False
+
+            # cross_attn + self_attn
+            layer = self.transformer_decoder.layers[i]
+            attn_masks = [attn_mask, None]
+            query_feat = layer(
+                query=query_feat,
+                key=decoder_inputs[level_idx],
+                value=decoder_inputs[level_idx],
+                query_pos=query_embed,
+                key_pos=decoder_positional_encodings[level_idx],
+                attn_masks=attn_masks,
+                query_key_padding_mask=None,
+                # here we do not apply masking on padded region
+                key_padding_mask=None,
+            )
+            cls_pred, mask_pred, attn_mask = self.forward_head(
+                query_feat, mask_features, multi_scale_memorys[(i + 1) % self.num_transformer_feat_level].shape[-2:]
+            )
+
+            cls_pred_list.append(cls_pred)
+            mask_pred_list.append(mask_pred)
+
+        return cls_pred_list, mask_pred_list
+
+    def forward_train(self, x, img_metas, gt_semantic_seg, gt_labels, gt_masks):
+        """Forward function for training mode.
+
+        Args:
+            x (list[Tensor]): Multi-level features from the upstream network,
+                each is a 4D-tensor.
+            img_metas (list[Dict]): List of image information.
+            gt_semantic_seg (list[tensor]):Each element is the ground truth
+                of semantic segmentation with the shape (N, H, W).
+            train_cfg (dict): The training config, which not been used in
+                maskformer.
+            gt_labels (list[Tensor]): Each element is ground truth labels of
+                each box, shape (num_gts,).
+            gt_masks (list[BitmapMasks]): Each element is masks of instances
+                of a image, shape (num_gts, h, w).
+
+        Returns:
+            losses (dict[str, Tensor]): a dictionary of loss components
+        """
+
+        # forward
+        all_cls_scores, all_mask_preds = self(x, img_metas)
+
+        # loss
+        losses = self.loss(all_cls_scores, all_mask_preds, gt_labels, gt_masks, img_metas)
+
+        return losses
+
+    def forward_test(self, inputs, img_metas, test_cfg):
+        """Test segment without test-time aumengtation.
+
+        Only the output of last decoder layers was used.
+
+        Args:
+            inputs (list[Tensor]): Multi-level features from the
+                upstream network, each is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+            test_cfg (dict): Testing config.
+
+        Returns:
+            seg_mask (Tensor): Predicted semantic segmentation logits.
+        """
+        all_cls_scores, all_mask_preds = self(inputs, img_metas)
+        cls_score, mask_pred = all_cls_scores[-1], all_mask_preds[-1]
+        ori_h, ori_w, _ = img_metas[0]["ori_shape"]
+
+        # semantic inference
+        cls_score = F.softmax(cls_score, dim=-1)[..., :-1]
+        mask_pred = mask_pred.sigmoid()
+        seg_mask = torch.einsum("bqc,bqhw->bchw", cls_score, mask_pred)
+        return seg_mask
diff --git a/src/dinov2/eval/segmentation_m2f/models/losses/__init__.py b/src/dinov2/eval/segmentation_m2f/models/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..229a887817372f4991b32354180592cfb236d728
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/losses/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .cross_entropy_loss import CrossEntropyLoss, binary_cross_entropy, cross_entropy, mask_cross_entropy
+from .dice_loss import DiceLoss
+from .match_costs import ClassificationCost, CrossEntropyLossCost, DiceCost
diff --git a/src/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py b/src/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a1f9dd4aa52ebe94cc527db36b1c7fa2f53813e
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py
@@ -0,0 +1,279 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmseg.models.builder import LOSSES
+from mmseg.models.losses.utils import get_class_weight, weight_reduce_loss
+
+
+def cross_entropy(
+    pred,
+    label,
+    weight=None,
+    class_weight=None,
+    reduction="mean",
+    avg_factor=None,
+    ignore_index=-100,
+    avg_non_ignore=False,
+):
+    """cross_entropy. The wrapper function for :func:`F.cross_entropy`
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, 1).
+        label (torch.Tensor): The learning label of the prediction.
+        weight (torch.Tensor, optional): Sample-wise loss weight.
+            Default: None.
+        class_weight (list[float], optional): The weight for each class.
+            Default: None.
+        reduction (str, optional): The method used to reduce the loss.
+            Options are 'none', 'mean' and 'sum'. Default: 'mean'.
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Default: None.
+        ignore_index (int): Specifies a target value that is ignored and
+            does not contribute to the input gradients. When
+            ``avg_non_ignore `` is ``True``, and the ``reduction`` is
+            ``''mean''``, the loss is averaged over non-ignored targets.
+            Defaults: -100.
+        avg_non_ignore (bool): The flag decides to whether the loss is
+            only averaged over non-ignored targets. Default: False.
+            `New in version 0.23.0.`
+    """
+
+    # class_weight is a manual rescaling weight given to each class.
+    # If given, has to be a Tensor of size C element-wise losses
+    loss = F.cross_entropy(pred, label, weight=class_weight, reduction="none", ignore_index=ignore_index)
+
+    # apply weights and do the reduction
+    # average loss over non-ignored elements
+    # pytorch's official cross_entropy average loss over non-ignored elements
+    # refer to https://github.com/pytorch/pytorch/blob/56b43f4fec1f76953f15a627694d4bba34588969/torch/nn/functional.py#L2660  # noqa
+    if (avg_factor is None) and avg_non_ignore and reduction == "mean":
+        avg_factor = label.numel() - (label == ignore_index).sum().item()
+    if weight is not None:
+        weight = weight.float()
+    loss = weight_reduce_loss(loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
+
+    return loss
+
+
+def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index):
+    """Expand onehot labels to match the size of prediction."""
+    bin_labels = labels.new_zeros(target_shape)
+    valid_mask = (labels >= 0) & (labels != ignore_index)
+    inds = torch.nonzero(valid_mask, as_tuple=True)
+
+    if inds[0].numel() > 0:
+        if labels.dim() == 3:
+            bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1
+        else:
+            bin_labels[inds[0], labels[valid_mask]] = 1
+
+    valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float()
+
+    if label_weights is None:
+        bin_label_weights = valid_mask
+    else:
+        bin_label_weights = label_weights.unsqueeze(1).expand(target_shape)
+        bin_label_weights = bin_label_weights * valid_mask
+
+    return bin_labels, bin_label_weights, valid_mask
+
+
+def binary_cross_entropy(
+    pred,
+    label,
+    weight=None,
+    reduction="mean",
+    avg_factor=None,
+    class_weight=None,
+    ignore_index=-100,
+    avg_non_ignore=False,
+    **kwargs,
+):
+    """Calculate the binary CrossEntropy loss.
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, 1).
+        label (torch.Tensor): The learning label of the prediction.
+            Note: In bce loss, label < 0 is invalid.
+        weight (torch.Tensor, optional): Sample-wise loss weight.
+        reduction (str, optional): The method used to reduce the loss.
+            Options are "none", "mean" and "sum".
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+        class_weight (list[float], optional): The weight for each class.
+        ignore_index (int): The label index to be ignored. Default: -100.
+        avg_non_ignore (bool): The flag decides to whether the loss is
+            only averaged over non-ignored targets. Default: False.
+            `New in version 0.23.0.`
+
+    Returns:
+        torch.Tensor: The calculated loss
+    """
+    if pred.size(1) == 1:
+        # For binary class segmentation, the shape of pred is
+        # [N, 1, H, W] and that of label is [N, H, W].
+        assert label.max() <= 1, "For pred with shape [N, 1, H, W], its label must have at " "most 2 classes"
+        pred = pred.squeeze()
+    if pred.dim() != label.dim():
+        assert (pred.dim() == 2 and label.dim() == 1) or (pred.dim() == 4 and label.dim() == 3), (
+            "Only pred shape [N, C], label shape [N] or pred shape [N, C, " "H, W], label shape [N, H, W] are supported"
+        )
+        # `weight` returned from `_expand_onehot_labels`
+        # has been treated for valid (non-ignore) pixels
+        label, weight, valid_mask = _expand_onehot_labels(label, weight, pred.shape, ignore_index)
+    else:
+        # should mask out the ignored elements
+        valid_mask = ((label >= 0) & (label != ignore_index)).float()
+        if weight is not None:
+            weight = weight * valid_mask
+        else:
+            weight = valid_mask
+    # average loss over non-ignored and valid elements
+    if reduction == "mean" and avg_factor is None and avg_non_ignore:
+        avg_factor = valid_mask.sum().item()
+
+    loss = F.binary_cross_entropy_with_logits(pred, label.float(), pos_weight=class_weight, reduction="none")
+    # do the reduction for the weighted loss
+    loss = weight_reduce_loss(loss, weight, reduction=reduction, avg_factor=avg_factor)
+
+    return loss
+
+
+def mask_cross_entropy(
+    pred, target, label, reduction="mean", avg_factor=None, class_weight=None, ignore_index=None, **kwargs
+):
+    """Calculate the CrossEntropy loss for masks.
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, C), C is the number
+            of classes.
+        target (torch.Tensor): The learning label of the prediction.
+        label (torch.Tensor): ``label`` indicates the class label of the mask'
+            corresponding object. This will be used to select the mask in the
+            of the class which the object belongs to when the mask prediction
+            if not class-agnostic.
+        reduction (str, optional): The method used to reduce the loss.
+            Options are "none", "mean" and "sum".
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+        class_weight (list[float], optional): The weight for each class.
+        ignore_index (None): Placeholder, to be consistent with other loss.
+            Default: None.
+
+    Returns:
+        torch.Tensor: The calculated loss
+    """
+    assert ignore_index is None, "BCE loss does not support ignore_index"
+    assert reduction == "mean" and avg_factor is None
+    num_rois = pred.size()[0]
+    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
+    pred_slice = pred[inds, label].squeeze(1)
+    return F.binary_cross_entropy_with_logits(pred_slice, target, weight=class_weight, reduction="mean")[None]
+
+
+@LOSSES.register_module(force=True)
+class CrossEntropyLoss(nn.Module):
+    """CrossEntropyLoss.
+
+    Args:
+        use_sigmoid (bool, optional): Whether the prediction uses sigmoid
+            of softmax. Defaults to False.
+        use_mask (bool, optional): Whether to use mask cross entropy loss.
+            Defaults to False.
+        reduction (str, optional): . Defaults to 'mean'.
+            Options are "none", "mean" and "sum".
+        class_weight (list[float] | str, optional): Weight of each class. If in
+            str format, read them from a file. Defaults to None.
+        loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
+        loss_name (str, optional): Name of the loss item. If you want this loss
+            item to be included into the backward graph, `loss_` must be the
+            prefix of the name. Defaults to 'loss_ce'.
+        avg_non_ignore (bool): The flag decides to whether the loss is
+            only averaged over non-ignored targets. Default: False.
+            `New in version 0.23.0.`
+    """
+
+    def __init__(
+        self,
+        use_sigmoid=False,
+        use_mask=False,
+        reduction="mean",
+        class_weight=None,
+        loss_weight=1.0,
+        loss_name="loss_ce",
+        avg_non_ignore=False,
+    ):
+        super(CrossEntropyLoss, self).__init__()
+        assert (use_sigmoid is False) or (use_mask is False)
+        self.use_sigmoid = use_sigmoid
+        self.use_mask = use_mask
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+        self.class_weight = get_class_weight(class_weight)
+        self.avg_non_ignore = avg_non_ignore
+        if not self.avg_non_ignore and self.reduction == "mean":
+            warnings.warn(
+                "Default ``avg_non_ignore`` is False, if you would like to "
+                "ignore the certain label and average loss over non-ignore "
+                "labels, which is the same with PyTorch official "
+                "cross_entropy, set ``avg_non_ignore=True``."
+            )
+
+        if self.use_sigmoid:
+            self.cls_criterion = binary_cross_entropy
+        elif self.use_mask:
+            self.cls_criterion = mask_cross_entropy
+        else:
+            self.cls_criterion = cross_entropy
+        self._loss_name = loss_name
+
+    def extra_repr(self):
+        """Extra repr."""
+        s = f"avg_non_ignore={self.avg_non_ignore}"
+        return s
+
+    def forward(
+        self, cls_score, label, weight=None, avg_factor=None, reduction_override=None, ignore_index=-100, **kwargs
+    ):
+        """Forward function."""
+        assert reduction_override in (None, "none", "mean", "sum")
+        reduction = reduction_override if reduction_override else self.reduction
+        if self.class_weight is not None:
+            class_weight = cls_score.new_tensor(self.class_weight)
+        else:
+            class_weight = None
+        # Note: for BCE loss, label < 0 is invalid.
+        loss_cls = self.loss_weight * self.cls_criterion(
+            cls_score,
+            label,
+            weight,
+            class_weight=class_weight,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            avg_non_ignore=self.avg_non_ignore,
+            ignore_index=ignore_index,
+            **kwargs,
+        )
+        return loss_cls
+
+    @property
+    def loss_name(self):
+        """Loss Name.
+
+        This function must be implemented and will return the name of this
+        loss function. This name will be used to combine different loss items
+        by simple sum operation. In addition, if you want this loss item to be
+        included into the backward graph, `loss_` must be the prefix of the
+        name.
+
+        Returns:
+            str: The name of this loss item.
+        """
+        return self._loss_name
diff --git a/src/dinov2/eval/segmentation_m2f/models/losses/dice_loss.py b/src/dinov2/eval/segmentation_m2f/models/losses/dice_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bc5ba893c502861032ed531283f225e183eb693
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/losses/dice_loss.py
@@ -0,0 +1,153 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from mmseg.models.builder import LOSSES
+from mmseg.models.losses.utils import weight_reduce_loss
+
+
+def dice_loss(pred, target, weight=None, eps=1e-3, reduction="mean", avg_factor=None):
+    """Calculate dice loss, which is proposed in
+    `V-Net: Fully Convolutional Neural Networks for Volumetric
+    Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_.
+
+    Args:
+        pred (torch.Tensor): The prediction, has a shape (n, *)
+        target (torch.Tensor): The learning label of the prediction,
+            shape (n, *), same shape of pred.
+        weight (torch.Tensor, optional): The weight of loss for each
+            prediction, has a shape (n,). Defaults to None.
+        eps (float): Avoid dividing by zero. Default: 1e-3.
+        reduction (str, optional): The method used to reduce the loss into
+            a scalar. Defaults to 'mean'.
+            Options are "none", "mean" and "sum".
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+    """
+
+    input = pred.flatten(1)
+    target = target.flatten(1).float()
+
+    a = torch.sum(input * target, 1)
+    b = torch.sum(input * input, 1) + eps
+    c = torch.sum(target * target, 1) + eps
+    d = (2 * a) / (b + c)
+    loss = 1 - d
+    if weight is not None:
+        assert weight.ndim == loss.ndim
+        assert len(weight) == len(pred)
+    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+    return loss
+
+
+def naive_dice_loss(pred, target, weight=None, eps=1e-3, reduction="mean", avg_factor=None):
+    """Calculate naive dice loss, the coefficient in the denominator is the
+    first power instead of the second power.
+
+    Args:
+        pred (torch.Tensor): The prediction, has a shape (n, *)
+        target (torch.Tensor): The learning label of the prediction,
+            shape (n, *), same shape of pred.
+        weight (torch.Tensor, optional): The weight of loss for each
+            prediction, has a shape (n,). Defaults to None.
+        eps (float): Avoid dividing by zero. Default: 1e-3.
+        reduction (str, optional): The method used to reduce the loss into
+            a scalar. Defaults to 'mean'.
+            Options are "none", "mean" and "sum".
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+    """
+    input = pred.flatten(1)
+    target = target.flatten(1).float()
+
+    a = torch.sum(input * target, 1)
+    b = torch.sum(input, 1)
+    c = torch.sum(target, 1)
+    d = (2 * a + eps) / (b + c + eps)
+    loss = 1 - d
+    if weight is not None:
+        assert weight.ndim == loss.ndim
+        assert len(weight) == len(pred)
+    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+    return loss
+
+
+@LOSSES.register_module(force=True)
+class DiceLoss(nn.Module):
+    def __init__(self, use_sigmoid=True, activate=True, reduction="mean", naive_dice=False, loss_weight=1.0, eps=1e-3):
+        """Dice Loss, there are two forms of dice loss is supported:
+
+            - the one proposed in `V-Net: Fully Convolutional Neural
+                Networks for Volumetric Medical Image Segmentation
+                <https://arxiv.org/abs/1606.04797>`_.
+            - the dice loss in which the power of the number in the
+                denominator is the first power instead of the second
+                power.
+
+        Args:
+            use_sigmoid (bool, optional): Whether to the prediction is
+                used for sigmoid or softmax. Defaults to True.
+            activate (bool): Whether to activate the predictions inside,
+                this will disable the inside sigmoid operation.
+                Defaults to True.
+            reduction (str, optional): The method used
+                to reduce the loss. Options are "none",
+                "mean" and "sum". Defaults to 'mean'.
+            naive_dice (bool, optional): If false, use the dice
+                loss defined in the V-Net paper, otherwise, use the
+                naive dice loss in which the power of the number in the
+                denominator is the first power instead of the second
+                power.Defaults to False.
+            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
+            eps (float): Avoid dividing by zero. Defaults to 1e-3.
+        """
+
+        super(DiceLoss, self).__init__()
+        self.use_sigmoid = use_sigmoid
+        self.reduction = reduction
+        self.naive_dice = naive_dice
+        self.loss_weight = loss_weight
+        self.eps = eps
+        self.activate = activate
+
+    def forward(self, pred, target, weight=None, reduction_override=None, avg_factor=None):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): The prediction, has a shape (n, *).
+            target (torch.Tensor): The label of the prediction,
+                shape (n, *), same shape of pred.
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction, has a shape (n,). Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Options are "none", "mean" and "sum".
+
+        Returns:
+            torch.Tensor: The calculated loss
+        """
+
+        assert reduction_override in (None, "none", "mean", "sum")
+        reduction = reduction_override if reduction_override else self.reduction
+
+        if self.activate:
+            if self.use_sigmoid:
+                pred = pred.sigmoid()
+            else:
+                raise NotImplementedError
+
+        if self.naive_dice:
+            loss = self.loss_weight * naive_dice_loss(
+                pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor
+            )
+        else:
+            loss = self.loss_weight * dice_loss(
+                pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor
+            )
+
+        return loss
diff --git a/src/dinov2/eval/segmentation_m2f/models/losses/match_costs.py b/src/dinov2/eval/segmentation_m2f/models/losses/match_costs.py
new file mode 100644
index 0000000000000000000000000000000000000000..4917d2a939c01398dd49c0d90b06f4c37d283ce0
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/losses/match_costs.py
@@ -0,0 +1,153 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn.functional as F
+
+from ..builder import MATCH_COST
+
+
+@MATCH_COST.register_module()
+class ClassificationCost:
+    """ClsSoftmaxCost.Borrow from
+    mmdet.core.bbox.match_costs.match_cost.ClassificationCost.
+
+     Args:
+         weight (int | float, optional): loss_weight
+
+     Examples:
+         >>> import torch
+         >>> self = ClassificationCost()
+         >>> cls_pred = torch.rand(4, 3)
+         >>> gt_labels = torch.tensor([0, 1, 2])
+         >>> factor = torch.tensor([10, 8, 10, 8])
+         >>> self(cls_pred, gt_labels)
+         tensor([[-0.3430, -0.3525, -0.3045],
+                [-0.3077, -0.2931, -0.3992],
+                [-0.3664, -0.3455, -0.2881],
+                [-0.3343, -0.2701, -0.3956]])
+    """
+
+    def __init__(self, weight=1.0):
+        self.weight = weight
+
+    def __call__(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classification logits, shape
+                [num_query, num_class].
+            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
+
+        Returns:
+            torch.Tensor: cls_cost value with weight
+        """
+        # Following the official DETR repo, contrary to the loss that
+        # NLL is used, we approximate it in 1 - cls_score[gt_label].
+        # The 1 is a constant that doesn't change the matching,
+        # so it can be omitted.
+        cls_score = cls_pred.softmax(-1)
+        cls_cost = -cls_score[:, gt_labels]
+        return cls_cost * self.weight
+
+
+@MATCH_COST.register_module()
+class DiceCost:
+    """Cost of mask assignments based on dice losses.
+
+    Args:
+        weight (int | float, optional): loss_weight. Defaults to 1.
+        pred_act (bool, optional): Whether to apply sigmoid to mask_pred.
+            Defaults to False.
+        eps (float, optional): default 1e-12.
+    """
+
+    def __init__(self, weight=1.0, pred_act=False, eps=1e-3):
+        self.weight = weight
+        self.pred_act = pred_act
+        self.eps = eps
+
+    def binary_mask_dice_loss(self, mask_preds, gt_masks):
+        """
+        Args:
+            mask_preds (Tensor): Mask prediction in shape (N1, H, W).
+            gt_masks (Tensor): Ground truth in shape (N2, H, W)
+                store 0 or 1, 0 for negative class and 1 for
+                positive class.
+
+        Returns:
+            Tensor: Dice cost matrix in shape (N1, N2).
+        """
+        mask_preds = mask_preds.reshape((mask_preds.shape[0], -1))
+        gt_masks = gt_masks.reshape((gt_masks.shape[0], -1)).float()
+        numerator = 2 * torch.einsum("nc,mc->nm", mask_preds, gt_masks)
+        denominator = mask_preds.sum(-1)[:, None] + gt_masks.sum(-1)[None, :]
+        loss = 1 - (numerator + self.eps) / (denominator + self.eps)
+        return loss
+
+    def __call__(self, mask_preds, gt_masks):
+        """
+        Args:
+            mask_preds (Tensor): Mask prediction logits in shape (N1, H, W).
+            gt_masks (Tensor): Ground truth in shape (N2, H, W).
+
+        Returns:
+            Tensor: Dice cost matrix in shape (N1, N2).
+        """
+        if self.pred_act:
+            mask_preds = mask_preds.sigmoid()
+        dice_cost = self.binary_mask_dice_loss(mask_preds, gt_masks)
+        return dice_cost * self.weight
+
+
+@MATCH_COST.register_module()
+class CrossEntropyLossCost:
+    """CrossEntropyLossCost.
+
+    Args:
+        weight (int | float, optional): loss weight. Defaults to 1.
+        use_sigmoid (bool, optional): Whether the prediction uses sigmoid
+                of softmax. Defaults to True.
+    """
+
+    def __init__(self, weight=1.0, use_sigmoid=True):
+        assert use_sigmoid, "use_sigmoid = False is not supported yet."
+        self.weight = weight
+        self.use_sigmoid = use_sigmoid
+
+    def _binary_cross_entropy(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): The prediction with shape (num_query, 1, *) or
+                (num_query, *).
+            gt_labels (Tensor): The learning label of prediction with
+                shape (num_gt, *).
+        Returns:
+            Tensor: Cross entropy cost matrix in shape (num_query, num_gt).
+        """
+        cls_pred = cls_pred.flatten(1).float()
+        gt_labels = gt_labels.flatten(1).float()
+        n = cls_pred.shape[1]
+        pos = F.binary_cross_entropy_with_logits(cls_pred, torch.ones_like(cls_pred), reduction="none")
+        neg = F.binary_cross_entropy_with_logits(cls_pred, torch.zeros_like(cls_pred), reduction="none")
+        cls_cost = torch.einsum("nc,mc->nm", pos, gt_labels) + torch.einsum("nc,mc->nm", neg, 1 - gt_labels)
+        cls_cost = cls_cost / n
+
+        return cls_cost
+
+    def __call__(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classification logits.
+            gt_labels (Tensor): Labels.
+        Returns:
+            Tensor: Cross entropy cost matrix with weight in
+                shape (num_query, num_gt).
+        """
+        if self.use_sigmoid:
+            cls_cost = self._binary_cross_entropy(cls_pred, gt_labels)
+        else:
+            raise NotImplementedError
+
+        return cls_cost * self.weight
diff --git a/src/dinov2/eval/segmentation_m2f/models/plugins/__init__.py b/src/dinov2/eval/segmentation_m2f/models/plugins/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..81a60db4de31238cb38e078683e5ca265839fe60
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/plugins/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .msdeformattn_pixel_decoder import MSDeformAttnPixelDecoder
diff --git a/src/dinov2/eval/segmentation_m2f/models/plugins/msdeformattn_pixel_decoder.py b/src/dinov2/eval/segmentation_m2f/models/plugins/msdeformattn_pixel_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..db1947175917f73f3f24184cb09c78e092d46ef8
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/plugins/msdeformattn_pixel_decoder.py
@@ -0,0 +1,242 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import PLUGIN_LAYERS, Conv2d, ConvModule, caffe2_xavier_init, normal_init, xavier_init
+from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer_sequence
+from mmcv.runner import BaseModule, ModuleList
+
+from ...core.anchor import MlvlPointGenerator
+from ..utils.transformer import MultiScaleDeformableAttention
+
+
+@PLUGIN_LAYERS.register_module()
+class MSDeformAttnPixelDecoder(BaseModule):
+    """Pixel decoder with multi-scale deformable attention.
+
+    Args:
+        in_channels (list[int] | tuple[int]): Number of channels in the
+            input feature maps.
+        strides (list[int] | tuple[int]): Output strides of feature from
+            backbone.
+        feat_channels (int): Number of channels for feature.
+        out_channels (int): Number of channels for output.
+        num_outs (int): Number of output scales.
+        norm_cfg (:obj:`mmcv.ConfigDict` | dict): Config for normalization.
+            Defaults to dict(type='GN', num_groups=32).
+        act_cfg (:obj:`mmcv.ConfigDict` | dict): Config for activation.
+            Defaults to dict(type='ReLU').
+        encoder (:obj:`mmcv.ConfigDict` | dict): Config for transformer
+            encoder. Defaults to `DetrTransformerEncoder`.
+        positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer encoder position encoding. Defaults to
+            dict(type='SinePositionalEncoding', num_feats=128,
+            normalize=True).
+        init_cfg (:obj:`mmcv.ConfigDict` | dict): Initialization config dict.
+    """
+
+    def __init__(
+        self,
+        in_channels=[256, 512, 1024, 2048],
+        strides=[4, 8, 16, 32],
+        feat_channels=256,
+        out_channels=256,
+        num_outs=3,
+        norm_cfg=dict(type="GN", num_groups=32),
+        act_cfg=dict(type="ReLU"),
+        encoder=dict(
+            type="DetrTransformerEncoder",
+            num_layers=6,
+            transformerlayers=dict(
+                type="BaseTransformerLayer",
+                attn_cfgs=dict(
+                    type="MultiScaleDeformableAttention",
+                    embed_dims=256,
+                    num_heads=8,
+                    num_levels=3,
+                    num_points=4,
+                    im2col_step=64,
+                    dropout=0.0,
+                    batch_first=False,
+                    norm_cfg=None,
+                    init_cfg=None,
+                ),
+                feedforward_channels=1024,
+                ffn_dropout=0.0,
+                operation_order=("self_attn", "norm", "ffn", "norm"),
+            ),
+            init_cfg=None,
+        ),
+        positional_encoding=dict(type="SinePositionalEncoding", num_feats=128, normalize=True),
+        init_cfg=None,
+    ):
+        super().__init__(init_cfg=init_cfg)
+        self.strides = strides
+        self.num_input_levels = len(in_channels)
+        self.num_encoder_levels = encoder.transformerlayers.attn_cfgs.num_levels
+        assert self.num_encoder_levels >= 1, "num_levels in attn_cfgs must be at least one"
+        input_conv_list = []
+        # from top to down (low to high resolution)
+        for i in range(self.num_input_levels - 1, self.num_input_levels - self.num_encoder_levels - 1, -1):
+            input_conv = ConvModule(
+                in_channels[i], feat_channels, kernel_size=1, norm_cfg=norm_cfg, act_cfg=None, bias=True
+            )
+            input_conv_list.append(input_conv)
+        self.input_convs = ModuleList(input_conv_list)
+
+        self.encoder = build_transformer_layer_sequence(encoder)
+        self.postional_encoding = build_positional_encoding(positional_encoding)
+        # high resolution to low resolution
+        self.level_encoding = nn.Embedding(self.num_encoder_levels, feat_channels)
+
+        # fpn-like structure
+        self.lateral_convs = ModuleList()
+        self.output_convs = ModuleList()
+        self.use_bias = norm_cfg is None
+        # from top to down (low to high resolution)
+        # fpn for the rest features that didn't pass in encoder
+        for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1, -1):
+            lateral_conv = ConvModule(
+                in_channels[i], feat_channels, kernel_size=1, bias=self.use_bias, norm_cfg=norm_cfg, act_cfg=None
+            )
+            output_conv = ConvModule(
+                feat_channels,
+                feat_channels,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=self.use_bias,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg,
+            )
+            self.lateral_convs.append(lateral_conv)
+            self.output_convs.append(output_conv)
+
+        self.mask_feature = Conv2d(feat_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+        self.num_outs = num_outs
+        self.point_generator = MlvlPointGenerator(strides)
+
+    def init_weights(self):
+        """Initialize weights."""
+        for i in range(0, self.num_encoder_levels):
+            xavier_init(self.input_convs[i].conv, gain=1, bias=0, distribution="uniform")
+
+        for i in range(0, self.num_input_levels - self.num_encoder_levels):
+            caffe2_xavier_init(self.lateral_convs[i].conv, bias=0)
+            caffe2_xavier_init(self.output_convs[i].conv, bias=0)
+
+        caffe2_xavier_init(self.mask_feature, bias=0)
+
+        normal_init(self.level_encoding, mean=0, std=1)
+        for p in self.encoder.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_normal_(p)
+
+        # init_weights defined in MultiScaleDeformableAttention
+        for layer in self.encoder.layers:
+            for attn in layer.attentions:
+                if isinstance(attn, MultiScaleDeformableAttention):
+                    attn.init_weights()
+
+    def forward(self, feats):
+        """
+        Args:
+            feats (list[Tensor]): Feature maps of each level. Each has
+                shape of (batch_size, c, h, w).
+
+        Returns:
+            tuple: A tuple containing the following:
+
+            - mask_feature (Tensor): shape (batch_size, c, h, w).
+            - multi_scale_features (list[Tensor]): Multi scale \
+                    features, each in shape (batch_size, c, h, w).
+        """
+        # generate padding mask for each level, for each image
+        batch_size = feats[0].shape[0]
+        encoder_input_list = []
+        padding_mask_list = []
+        level_positional_encoding_list = []
+        spatial_shapes = []
+        reference_points_list = []
+        for i in range(self.num_encoder_levels):
+            level_idx = self.num_input_levels - i - 1
+            feat = feats[level_idx]
+            feat_projected = self.input_convs[i](feat)
+            h, w = feat.shape[-2:]
+
+            # no padding
+            padding_mask_resized = feat.new_zeros((batch_size,) + feat.shape[-2:], dtype=torch.bool)
+            pos_embed = self.postional_encoding(padding_mask_resized)
+            level_embed = self.level_encoding.weight[i]
+            level_pos_embed = level_embed.view(1, -1, 1, 1) + pos_embed
+            # (h_i * w_i, 2)
+            reference_points = self.point_generator.single_level_grid_priors(
+                feat.shape[-2:], level_idx, device=feat.device
+            )
+            # normalize
+            factor = feat.new_tensor([[w, h]]) * self.strides[level_idx]
+            reference_points = reference_points / factor
+
+            # shape (batch_size, c, h_i, w_i) -> (h_i * w_i, batch_size, c)
+            feat_projected = feat_projected.flatten(2).permute(2, 0, 1)
+            level_pos_embed = level_pos_embed.flatten(2).permute(2, 0, 1)
+            padding_mask_resized = padding_mask_resized.flatten(1)
+
+            encoder_input_list.append(feat_projected)
+            padding_mask_list.append(padding_mask_resized)
+            level_positional_encoding_list.append(level_pos_embed)
+            spatial_shapes.append(feat.shape[-2:])
+            reference_points_list.append(reference_points)
+        # shape (batch_size, total_num_query),
+        # total_num_query=sum([., h_i * w_i,.])
+        padding_masks = torch.cat(padding_mask_list, dim=1)
+        # shape (total_num_query, batch_size, c)
+        encoder_inputs = torch.cat(encoder_input_list, dim=0)
+        level_positional_encodings = torch.cat(level_positional_encoding_list, dim=0)
+        device = encoder_inputs.device
+        # shape (num_encoder_levels, 2), from low
+        # resolution to high resolution
+        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=device)
+        # shape (0, h_0*w_0, h_0*w_0+h_1*w_1, ...)
+        level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
+        reference_points = torch.cat(reference_points_list, dim=0)
+        reference_points = reference_points[None, :, None].repeat(batch_size, 1, self.num_encoder_levels, 1)
+        valid_radios = reference_points.new_ones((batch_size, self.num_encoder_levels, 2))
+        # shape (num_total_query, batch_size, c)
+        memory = self.encoder(
+            query=encoder_inputs,
+            key=None,
+            value=None,
+            query_pos=level_positional_encodings,
+            key_pos=None,
+            attn_masks=None,
+            key_padding_mask=None,
+            query_key_padding_mask=padding_masks,
+            spatial_shapes=spatial_shapes,
+            reference_points=reference_points,
+            level_start_index=level_start_index,
+            valid_radios=valid_radios,
+        )
+        # (num_total_query, batch_size, c) -> (batch_size, c, num_total_query)
+        memory = memory.permute(1, 2, 0)
+
+        # from low resolution to high resolution
+        num_query_per_level = [e[0] * e[1] for e in spatial_shapes]
+        outs = torch.split(memory, num_query_per_level, dim=-1)
+        outs = [x.reshape(batch_size, -1, spatial_shapes[i][0], spatial_shapes[i][1]) for i, x in enumerate(outs)]
+
+        for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1, -1):
+            x = feats[i]
+            cur_feat = self.lateral_convs[i](x)
+            y = cur_feat + F.interpolate(outs[-1], size=cur_feat.shape[-2:], mode="bilinear", align_corners=False)
+            y = self.output_convs[i](y)
+            outs.append(y)
+        multi_scale_features = outs[: self.num_outs]
+
+        mask_feature = self.mask_feature(outs[-1])
+        return mask_feature, multi_scale_features
diff --git a/src/dinov2/eval/segmentation_m2f/models/segmentors/__init__.py b/src/dinov2/eval/segmentation_m2f/models/segmentors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..adf0062691e4889612e118f28ced853cd0bc33db
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/segmentors/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .encoder_decoder_mask2former import EncoderDecoderMask2Former
diff --git a/src/dinov2/eval/segmentation_m2f/models/segmentors/encoder_decoder_mask2former.py b/src/dinov2/eval/segmentation_m2f/models/segmentors/encoder_decoder_mask2former.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfe572c9d317303bff8d51b85217d144906ebfe7
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/segmentors/encoder_decoder_mask2former.py
@@ -0,0 +1,271 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmseg.core import add_prefix
+from mmseg.models import builder
+from mmseg.models.builder import SEGMENTORS
+from mmseg.models.segmentors.base import BaseSegmentor
+from mmseg.ops import resize
+
+
+@SEGMENTORS.register_module()
+class EncoderDecoderMask2Former(BaseSegmentor):
+    """Encoder Decoder segmentors.
+
+    EncoderDecoder typically consists of backbone, decode_head, auxiliary_head.
+    Note that auxiliary_head is only used for deep supervision during training,
+    which could be dumped during inference.
+    """
+
+    def __init__(
+        self,
+        backbone,
+        decode_head,
+        neck=None,
+        auxiliary_head=None,
+        train_cfg=None,
+        test_cfg=None,
+        pretrained=None,
+        init_cfg=None,
+    ):
+        super(EncoderDecoderMask2Former, self).__init__(init_cfg)
+        if pretrained is not None:
+            assert backbone.get("pretrained") is None, "both backbone and segmentor set pretrained weight"
+            backbone.pretrained = pretrained
+        self.backbone = builder.build_backbone(backbone)
+        if neck is not None:
+            self.neck = builder.build_neck(neck)
+        decode_head.update(train_cfg=train_cfg)
+        decode_head.update(test_cfg=test_cfg)
+        self._init_decode_head(decode_head)
+        self._init_auxiliary_head(auxiliary_head)
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+        assert self.with_decode_head
+
+    def _init_decode_head(self, decode_head):
+        """Initialize ``decode_head``"""
+        self.decode_head = builder.build_head(decode_head)
+        self.align_corners = self.decode_head.align_corners
+        self.num_classes = self.decode_head.num_classes
+
+    def _init_auxiliary_head(self, auxiliary_head):
+        """Initialize ``auxiliary_head``"""
+        if auxiliary_head is not None:
+            if isinstance(auxiliary_head, list):
+                self.auxiliary_head = nn.ModuleList()
+                for head_cfg in auxiliary_head:
+                    self.auxiliary_head.append(builder.build_head(head_cfg))
+            else:
+                self.auxiliary_head = builder.build_head(auxiliary_head)
+
+    def extract_feat(self, img):
+        """Extract features from images."""
+        x = self.backbone(img)
+        if self.with_neck:
+            x = self.neck(x)
+        return x
+
+    def encode_decode(self, img, img_metas):
+        """Encode images with backbone and decode into a semantic segmentation
+        map of the same size as input."""
+        x = self.extract_feat(img)
+        out = self._decode_head_forward_test(x, img_metas)
+        out = resize(input=out, size=img.shape[2:], mode="bilinear", align_corners=self.align_corners)
+        return out
+
+    def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg, **kwargs):
+        """Run forward function and calculate loss for decode head in
+        training."""
+        losses = dict()
+        loss_decode = self.decode_head.forward_train(x, img_metas, gt_semantic_seg, **kwargs)
+
+        losses.update(add_prefix(loss_decode, "decode"))
+        return losses
+
+    def _decode_head_forward_test(self, x, img_metas):
+        """Run forward function and calculate loss for decode head in
+        inference."""
+        seg_logits = self.decode_head.forward_test(x, img_metas, self.test_cfg)
+        return seg_logits
+
+    def _auxiliary_head_forward_train(self, x, img_metas, gt_semantic_seg):
+        """Run forward function and calculate loss for auxiliary head in
+        training."""
+        losses = dict()
+        if isinstance(self.auxiliary_head, nn.ModuleList):
+            for idx, aux_head in enumerate(self.auxiliary_head):
+                loss_aux = aux_head.forward_train(x, img_metas, gt_semantic_seg, self.train_cfg)
+                losses.update(add_prefix(loss_aux, f"aux_{idx}"))
+        else:
+            loss_aux = self.auxiliary_head.forward_train(x, img_metas, gt_semantic_seg, self.train_cfg)
+            losses.update(add_prefix(loss_aux, "aux"))
+
+        return losses
+
+    def forward_dummy(self, img):
+        """Dummy forward function."""
+        seg_logit = self.encode_decode(img, None)
+
+        return seg_logit
+
+    def forward_train(self, img, img_metas, gt_semantic_seg, **kwargs):
+        """Forward function for training.
+
+        Args:
+            img (Tensor): Input images.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmseg/datasets/pipelines/formatting.py:Collect`.
+            gt_semantic_seg (Tensor): Semantic segmentation masks
+                used if the architecture supports semantic segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+
+        x = self.extract_feat(img)
+
+        losses = dict()
+
+        loss_decode = self._decode_head_forward_train(x, img_metas, gt_semantic_seg, **kwargs)
+        losses.update(loss_decode)
+
+        if self.with_auxiliary_head:
+            loss_aux = self._auxiliary_head_forward_train(x, img_metas, gt_semantic_seg)
+            losses.update(loss_aux)
+
+        return losses
+
+    def slide_inference(self, img, img_meta, rescale):
+        """Inference by sliding-window with overlap.
+
+        If h_crop > h_img or w_crop > w_img, the small patch will be used to
+        decode without padding.
+        """
+
+        h_stride, w_stride = self.test_cfg.stride
+        h_crop, w_crop = self.test_cfg.crop_size
+        batch_size, _, h_img, w_img = img.size()
+        num_classes = self.num_classes
+        h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
+        w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
+        preds = img.new_zeros((batch_size, num_classes, h_img, w_img))
+        count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
+        for h_idx in range(h_grids):
+            for w_idx in range(w_grids):
+                y1 = h_idx * h_stride
+                x1 = w_idx * w_stride
+                y2 = min(y1 + h_crop, h_img)
+                x2 = min(x1 + w_crop, w_img)
+                y1 = max(y2 - h_crop, 0)
+                x1 = max(x2 - w_crop, 0)
+                crop_img = img[:, :, y1:y2, x1:x2]
+                crop_seg_logit = self.encode_decode(crop_img, img_meta)
+                preds += F.pad(crop_seg_logit, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2)))
+
+                count_mat[:, :, y1:y2, x1:x2] += 1
+        assert (count_mat == 0).sum() == 0
+        if torch.onnx.is_in_onnx_export():
+            # cast count_mat to constant while exporting to ONNX
+            count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device)
+        preds = preds / count_mat
+        if rescale:
+            preds = resize(
+                preds,
+                size=img_meta[0]["ori_shape"][:2],
+                mode="bilinear",
+                align_corners=self.align_corners,
+                warning=False,
+            )
+        return preds
+
+    def whole_inference(self, img, img_meta, rescale):
+        """Inference with full image."""
+
+        seg_logit = self.encode_decode(img, img_meta)
+        if rescale:
+            # support dynamic shape for onnx
+            if torch.onnx.is_in_onnx_export():
+                size = img.shape[2:]
+            else:
+                size = img_meta[0]["ori_shape"][:2]
+            seg_logit = resize(seg_logit, size=size, mode="bilinear", align_corners=self.align_corners, warning=False)
+
+        return seg_logit
+
+    def inference(self, img, img_meta, rescale):
+        """Inference with slide/whole style.
+
+        Args:
+            img (Tensor): The input image of shape (N, 3, H, W).
+            img_meta (dict): Image info dict where each dict has: 'img_shape',
+                'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmseg/datasets/pipelines/formatting.py:Collect`.
+            rescale (bool): Whether rescale back to original shape.
+
+        Returns:
+            Tensor: The output segmentation map.
+        """
+
+        assert self.test_cfg.mode in ["slide", "whole"]
+        ori_shape = img_meta[0]["ori_shape"]
+        assert all(_["ori_shape"] == ori_shape for _ in img_meta)
+        if self.test_cfg.mode == "slide":
+            seg_logit = self.slide_inference(img, img_meta, rescale)
+        else:
+            seg_logit = self.whole_inference(img, img_meta, rescale)
+        output = F.softmax(seg_logit, dim=1)
+        flip = img_meta[0]["flip"]
+        if flip:
+            flip_direction = img_meta[0]["flip_direction"]
+            assert flip_direction in ["horizontal", "vertical"]
+            if flip_direction == "horizontal":
+                output = output.flip(dims=(3,))
+            elif flip_direction == "vertical":
+                output = output.flip(dims=(2,))
+
+        return output
+
+    def simple_test(self, img, img_meta, rescale=True):
+        """Simple test with single image."""
+        seg_logit = self.inference(img, img_meta, rescale)
+        seg_pred = seg_logit.argmax(dim=1)
+        if torch.onnx.is_in_onnx_export():
+            # our inference backend only support 4D output
+            seg_pred = seg_pred.unsqueeze(0)
+            return seg_pred
+        seg_pred = seg_pred.cpu().numpy()
+        # unravel batch dim
+        seg_pred = list(seg_pred)
+        return seg_pred
+
+    def aug_test(self, imgs, img_metas, rescale=True):
+        """Test with augmentations.
+
+        Only rescale=True is supported.
+        """
+        # aug_test rescale all imgs back to ori_shape for now
+        assert rescale
+        # to save memory, we get augmented seg logit inplace
+        seg_logit = self.inference(imgs[0], img_metas[0], rescale)
+        for i in range(1, len(imgs)):
+            cur_seg_logit = self.inference(imgs[i], img_metas[i], rescale)
+            seg_logit += cur_seg_logit
+        seg_logit /= len(imgs)
+        seg_pred = seg_logit.argmax(dim=1)
+        seg_pred = seg_pred.cpu().numpy()
+        # unravel batch dim
+        seg_pred = list(seg_pred)
+        return seg_pred
diff --git a/src/dinov2/eval/segmentation_m2f/models/utils/__init__.py b/src/dinov2/eval/segmentation_m2f/models/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7fdc1668b1015c8feea8fa1a4691bc0ebdbd936
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/utils/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .assigner import MaskHungarianAssigner
+from .point_sample import get_uncertain_point_coords_with_randomness
+from .positional_encoding import LearnedPositionalEncoding, SinePositionalEncoding
+from .transformer import DetrTransformerDecoder, DetrTransformerDecoderLayer, DynamicConv, Transformer
diff --git a/src/dinov2/eval/segmentation_m2f/models/utils/assigner.py b/src/dinov2/eval/segmentation_m2f/models/utils/assigner.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cb08fc1bb2e36336989b45a1d3850f260c05963
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/utils/assigner.py
@@ -0,0 +1,157 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from abc import ABCMeta, abstractmethod
+
+import torch
+
+from ..builder import MASK_ASSIGNERS, build_match_cost
+
+try:
+    from scipy.optimize import linear_sum_assignment
+except ImportError:
+    linear_sum_assignment = None
+
+
+class AssignResult(metaclass=ABCMeta):
+    """Collection of assign results."""
+
+    def __init__(self, num_gts, gt_inds, labels):
+        self.num_gts = num_gts
+        self.gt_inds = gt_inds
+        self.labels = labels
+
+    @property
+    def info(self):
+        info = {
+            "num_gts": self.num_gts,
+            "gt_inds": self.gt_inds,
+            "labels": self.labels,
+        }
+        return info
+
+
+class BaseAssigner(metaclass=ABCMeta):
+    """Base assigner that assigns boxes to ground truth boxes."""
+
+    @abstractmethod
+    def assign(self, masks, gt_masks, gt_masks_ignore=None, gt_labels=None):
+        """Assign boxes to either a ground truth boxes or a negative boxes."""
+        pass
+
+
+@MASK_ASSIGNERS.register_module()
+class MaskHungarianAssigner(BaseAssigner):
+    """Computes one-to-one matching between predictions and ground truth for
+    mask.
+
+    This class computes an assignment between the targets and the predictions
+    based on the costs. The costs are weighted sum of three components:
+    classification cost, regression L1 cost and regression iou cost. The
+    targets don't include the no_object, so generally there are more
+    predictions than targets. After the one-to-one matching, the un-matched
+    are treated as backgrounds. Thus each query prediction will be assigned
+    with `0` or a positive integer indicating the ground truth index:
+
+    - 0: negative sample, no assigned gt
+    - positive integer: positive sample, index (1-based) of assigned gt
+
+    Args:
+        cls_cost (obj:`mmcv.ConfigDict`|dict): Classification cost config.
+        mask_cost (obj:`mmcv.ConfigDict`|dict): Mask cost config.
+        dice_cost (obj:`mmcv.ConfigDict`|dict): Dice cost config.
+    """
+
+    def __init__(
+        self,
+        cls_cost=dict(type="ClassificationCost", weight=1.0),
+        dice_cost=dict(type="DiceCost", weight=1.0),
+        mask_cost=dict(type="MaskFocalCost", weight=1.0),
+    ):
+        self.cls_cost = build_match_cost(cls_cost)
+        self.dice_cost = build_match_cost(dice_cost)
+        self.mask_cost = build_match_cost(mask_cost)
+
+    def assign(self, cls_pred, mask_pred, gt_labels, gt_masks, img_meta, gt_masks_ignore=None, eps=1e-7):
+        """Computes one-to-one matching based on the weighted costs.
+
+        This method assign each query prediction to a ground truth or
+        background. The `assigned_gt_inds` with -1 means don't care,
+        0 means negative sample, and positive number is the index (1-based)
+        of assigned gt.
+        The assignment is done in the following steps, the order matters.
+
+        1. assign every prediction to -1
+        2. compute the weighted costs
+        3. do Hungarian matching on CPU based on the costs
+        4. assign all to 0 (background) first, then for each matched pair
+           between predictions and gts, treat this prediction as foreground
+           and assign the corresponding gt index (plus 1) to it.
+
+        Args:
+            mask_pred (Tensor): Predicted mask, shape [num_query, h, w]
+            cls_pred (Tensor): Predicted classification logits, shape
+                [num_query, num_class].
+            gt_masks (Tensor): Ground truth mask, shape [num_gt, h, w].
+            gt_labels (Tensor): Label of `gt_masks`, shape (num_gt,).
+            img_meta (dict): Meta information for current image.
+            gt_masks_ignore (Tensor, optional): Ground truth masks that are
+                labelled as `ignored`. Default None.
+            eps (int | float, optional): A value added to the denominator for
+                numerical stability. Default 1e-7.
+
+        Returns:
+            :obj:`AssignResult`: The assigned result.
+        """
+        assert gt_masks_ignore is None, "Only case when gt_masks_ignore is None is supported."
+        num_gts, num_queries = gt_labels.shape[0], cls_pred.shape[0]
+
+        # 1. assign -1 by default
+        assigned_gt_inds = cls_pred.new_full((num_queries,), -1, dtype=torch.long)
+        assigned_labels = cls_pred.new_full((num_queries,), -1, dtype=torch.long)
+        if num_gts == 0 or num_queries == 0:
+            # No ground truth or boxes, return empty assignment
+            if num_gts == 0:
+                # No ground truth, assign all to background
+                assigned_gt_inds[:] = 0
+            return AssignResult(num_gts, assigned_gt_inds, labels=assigned_labels)
+
+        # 2. compute the weighted costs
+        # classification and maskcost.
+        if self.cls_cost.weight != 0 and cls_pred is not None:
+            cls_cost = self.cls_cost(cls_pred, gt_labels)
+        else:
+            cls_cost = 0
+
+        if self.mask_cost.weight != 0:
+            # mask_pred shape = [nq, h, w]
+            # gt_mask shape = [ng, h, w]
+            # mask_cost shape = [nq, ng]
+            mask_cost = self.mask_cost(mask_pred, gt_masks)
+        else:
+            mask_cost = 0
+
+        if self.dice_cost.weight != 0:
+            dice_cost = self.dice_cost(mask_pred, gt_masks)
+        else:
+            dice_cost = 0
+        cost = cls_cost + mask_cost + dice_cost
+
+        # 3. do Hungarian matching on CPU using linear_sum_assignment
+        cost = cost.detach().cpu()
+        if linear_sum_assignment is None:
+            raise ImportError('Please run "pip install scipy" ' "to install scipy first.")
+
+        matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
+        matched_row_inds = torch.from_numpy(matched_row_inds).to(cls_pred.device)
+        matched_col_inds = torch.from_numpy(matched_col_inds).to(cls_pred.device)
+
+        # 4. assign backgrounds and foregrounds
+        # assign all indices to backgrounds first
+        assigned_gt_inds[:] = 0
+        # assign foregrounds based on matching results
+        assigned_gt_inds[matched_row_inds] = matched_col_inds + 1
+        assigned_labels[matched_row_inds] = gt_labels[matched_col_inds]
+        return AssignResult(num_gts, assigned_gt_inds, labels=assigned_labels)
diff --git a/src/dinov2/eval/segmentation_m2f/models/utils/point_sample.py b/src/dinov2/eval/segmentation_m2f/models/utils/point_sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f1134082bafb51432618a9632592db070f87284
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/utils/point_sample.py
@@ -0,0 +1,86 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+from mmcv.ops import point_sample
+
+
+def get_uncertainty(mask_pred, labels):
+    """Estimate uncertainty based on pred logits.
+
+    We estimate uncertainty as L1 distance between 0.0 and the logits
+    prediction in 'mask_pred' for the foreground class in `classes`.
+
+    Args:
+        mask_pred (Tensor): mask predication logits, shape (num_rois,
+            num_classes, mask_height, mask_width).
+
+        labels (list[Tensor]): Either predicted or ground truth label for
+            each predicted mask, of length num_rois.
+
+    Returns:
+        scores (Tensor): Uncertainty scores with the most uncertain
+            locations having the highest uncertainty score,
+            shape (num_rois, 1, mask_height, mask_width)
+    """
+    if mask_pred.shape[1] == 1:
+        gt_class_logits = mask_pred.clone()
+    else:
+        inds = torch.arange(mask_pred.shape[0], device=mask_pred.device)
+        gt_class_logits = mask_pred[inds, labels].unsqueeze(1)
+    return -torch.abs(gt_class_logits)
+
+
+def get_uncertain_point_coords_with_randomness(
+    mask_pred, labels, num_points, oversample_ratio, importance_sample_ratio
+):
+    """Get ``num_points`` most uncertain points with random points during
+    train.
+
+    Sample points in [0, 1] x [0, 1] coordinate space based on their
+    uncertainty. The uncertainties are calculated for each point using
+    'get_uncertainty()' function that takes point's logit prediction as
+    input.
+
+    Args:
+        mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
+            mask_height, mask_width) for class-specific or class-agnostic
+            prediction.
+        labels (list): The ground truth class for each instance.
+        num_points (int): The number of points to sample.
+        oversample_ratio (int): Oversampling parameter.
+        importance_sample_ratio (float): Ratio of points that are sampled
+            via importnace sampling.
+
+    Returns:
+        point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
+            that contains the coordinates sampled points.
+    """
+    assert oversample_ratio >= 1
+    assert 0 <= importance_sample_ratio <= 1
+    batch_size = mask_pred.shape[0]
+    num_sampled = int(num_points * oversample_ratio)
+    point_coords = torch.rand(batch_size, num_sampled, 2, device=mask_pred.device)
+    point_logits = point_sample(mask_pred, point_coords)
+    # It is crucial to calculate uncertainty based on the sampled
+    # prediction value for the points. Calculating uncertainties of the
+    # coarse predictions first and sampling them for points leads to
+    # incorrect results.  To illustrate this: assume uncertainty func(
+    # logits)=-abs(logits), a sampled point between two coarse
+    # predictions with -1 and 1 logits has 0 logits, and therefore 0
+    # uncertainty value. However, if we calculate uncertainties for the
+    # coarse predictions first, both will have -1 uncertainty,
+    # and sampled point will get -1 uncertainty.
+    point_uncertainties = get_uncertainty(point_logits, labels)
+    num_uncertain_points = int(importance_sample_ratio * num_points)
+    num_random_points = num_points - num_uncertain_points
+    idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
+    shift = num_sampled * torch.arange(batch_size, dtype=torch.long, device=mask_pred.device)
+    idx += shift[:, None]
+    point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(batch_size, num_uncertain_points, 2)
+    if num_random_points > 0:
+        rand_roi_coords = torch.rand(batch_size, num_random_points, 2, device=mask_pred.device)
+        point_coords = torch.cat((point_coords, rand_roi_coords), dim=1)
+    return point_coords
diff --git a/src/dinov2/eval/segmentation_m2f/models/utils/positional_encoding.py b/src/dinov2/eval/segmentation_m2f/models/utils/positional_encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf5d6fabe946d06fe97cc799da47bae93758b34e
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/utils/positional_encoding.py
@@ -0,0 +1,152 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING
+from mmcv.runner import BaseModule
+
+
+@POSITIONAL_ENCODING.register_module()
+class SinePositionalEncoding(BaseModule):
+    """Position encoding with sine and cosine functions.
+
+    See `End-to-End Object Detection with Transformers
+    <https://arxiv.org/pdf/2005.12872>`_ for details.
+
+    Args:
+        num_feats (int): The feature dimension for each position
+            along x-axis or y-axis. Note the final returned dimension
+            for each position is 2 times of this value.
+        temperature (int, optional): The temperature used for scaling
+            the position embedding. Defaults to 10000.
+        normalize (bool, optional): Whether to normalize the position
+            embedding. Defaults to False.
+        scale (float, optional): A scale factor that scales the position
+            embedding. The scale will be used only when `normalize` is True.
+            Defaults to 2*pi.
+        eps (float, optional): A value added to the denominator for
+            numerical stability. Defaults to 1e-6.
+        offset (float): offset add to embed when do the normalization.
+            Defaults to 0.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(
+        self, num_feats, temperature=10000, normalize=False, scale=2 * math.pi, eps=1e-6, offset=0.0, init_cfg=None
+    ):
+        super(SinePositionalEncoding, self).__init__(init_cfg)
+        if normalize:
+            assert isinstance(scale, (float, int)), (
+                "when normalize is set," "scale should be provided and in float or int type, " f"found {type(scale)}"
+            )
+        self.num_feats = num_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        self.scale = scale
+        self.eps = eps
+        self.offset = offset
+
+    def forward(self, mask):
+        """Forward function for `SinePositionalEncoding`.
+
+        Args:
+            mask (Tensor): ByteTensor mask. Non-zero values representing
+                ignored positions, while zero values means valid positions
+                for this image. Shape [bs, h, w].
+
+        Returns:
+            pos (Tensor): Returned position embedding with shape
+                [bs, num_feats*2, h, w].
+        """
+        # For convenience of exporting to ONNX, it's required to convert
+        # `masks` from bool to int.
+        mask = mask.to(torch.int)
+        not_mask = 1 - mask  # logical_not
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            y_embed = (y_embed + self.offset) / (y_embed[:, -1:, :] + self.eps) * self.scale
+            x_embed = (x_embed + self.offset) / (x_embed[:, :, -1:] + self.eps) * self.scale
+        dim_t = torch.arange(self.num_feats, dtype=torch.float32, device=mask.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_feats)
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        # use `view` instead of `flatten` for dynamically exporting to ONNX
+        B, H, W = mask.size()
+        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1)
+        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+    def __repr__(self):
+        """str: a string that describes the module"""
+        repr_str = self.__class__.__name__
+        repr_str += f"(num_feats={self.num_feats}, "
+        repr_str += f"temperature={self.temperature}, "
+        repr_str += f"normalize={self.normalize}, "
+        repr_str += f"scale={self.scale}, "
+        repr_str += f"eps={self.eps})"
+        return repr_str
+
+
+@POSITIONAL_ENCODING.register_module()
+class LearnedPositionalEncoding(BaseModule):
+    """Position embedding with learnable embedding weights.
+
+    Args:
+        num_feats (int): The feature dimension for each position
+            along x-axis or y-axis. The final returned dimension for
+            each position is 2 times of this value.
+        row_num_embed (int, optional): The dictionary size of row embeddings.
+            Default 50.
+        col_num_embed (int, optional): The dictionary size of col embeddings.
+            Default 50.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self, num_feats, row_num_embed=50, col_num_embed=50, init_cfg=dict(type="Uniform", layer="Embedding")):
+        super(LearnedPositionalEncoding, self).__init__(init_cfg)
+        self.row_embed = nn.Embedding(row_num_embed, num_feats)
+        self.col_embed = nn.Embedding(col_num_embed, num_feats)
+        self.num_feats = num_feats
+        self.row_num_embed = row_num_embed
+        self.col_num_embed = col_num_embed
+
+    def forward(self, mask):
+        """Forward function for `LearnedPositionalEncoding`.
+
+        Args:
+            mask (Tensor): ByteTensor mask. Non-zero values representing
+                ignored positions, while zero values means valid positions
+                for this image. Shape [bs, h, w].
+
+        Returns:
+            pos (Tensor): Returned position embedding with shape
+                [bs, num_feats*2, h, w].
+        """
+        h, w = mask.shape[-2:]
+        x = torch.arange(w, device=mask.device)
+        y = torch.arange(h, device=mask.device)
+        x_embed = self.col_embed(x)
+        y_embed = self.row_embed(y)
+        pos = (
+            torch.cat((x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat(1, w, 1)), dim=-1)
+            .permute(2, 0, 1)
+            .unsqueeze(0)
+            .repeat(mask.shape[0], 1, 1, 1)
+        )
+        return pos
+
+    def __repr__(self):
+        """str: a string that describes the module"""
+        repr_str = self.__class__.__name__
+        repr_str += f"(num_feats={self.num_feats}, "
+        repr_str += f"row_num_embed={self.row_num_embed}, "
+        repr_str += f"col_num_embed={self.col_num_embed})"
+        return repr_str
diff --git a/src/dinov2/eval/segmentation_m2f/models/utils/transformer.py b/src/dinov2/eval/segmentation_m2f/models/utils/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8befe6011a34d5ccecb82c8b17b61e19f732f96b
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/models/utils/transformer.py
@@ -0,0 +1,989 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import math
+import warnings
+from typing import Sequence
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+from mmcv.cnn import Linear, build_activation_layer, build_norm_layer, xavier_init
+from mmcv.cnn.bricks.drop import build_dropout
+from mmcv.cnn.bricks.registry import FEEDFORWARD_NETWORK, TRANSFORMER_LAYER, TRANSFORMER_LAYER_SEQUENCE
+from mmcv.cnn.bricks.transformer import BaseTransformerLayer, TransformerLayerSequence, build_transformer_layer_sequence
+from mmcv.runner.base_module import BaseModule, Sequential
+from mmcv.utils import deprecated_api_warning, to_2tuple
+from torch.nn.init import normal_
+
+from ..builder import TRANSFORMER
+
+try:
+    from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention
+
+except ImportError:
+    warnings.warn(
+        "`MultiScaleDeformableAttention` in MMCV has been moved to "
+        "`mmcv.ops.multi_scale_deform_attn`, please update your MMCV"
+    )
+    from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention
+
+
+class AdaptivePadding(nn.Module):
+    """Applies padding to input (if needed) so that input can get fully covered
+    by filter you specified. It support two modes "same" and "corner". The
+    "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around
+    input. The "corner"  mode would pad zero to bottom right.
+
+    Args:
+        kernel_size (int | tuple): Size of the kernel:
+        stride (int | tuple): Stride of the filter. Default: 1:
+        dilation (int | tuple): Spacing between kernel elements.
+            Default: 1
+        padding (str): Support "same" and "corner", "corner" mode
+            would pad zero to bottom right, and "same" mode would
+            pad zero around input. Default: "corner".
+    Example:
+        >>> kernel_size = 16
+        >>> stride = 16
+        >>> dilation = 1
+        >>> input = torch.rand(1, 1, 15, 17)
+        >>> adap_pad = AdaptivePadding(
+        >>>     kernel_size=kernel_size,
+        >>>     stride=stride,
+        >>>     dilation=dilation,
+        >>>     padding="corner")
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+        >>> input = torch.rand(1, 1, 16, 17)
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+    """
+
+    def __init__(self, kernel_size=1, stride=1, dilation=1, padding="corner"):
+
+        super(AdaptivePadding, self).__init__()
+
+        assert padding in ("same", "corner")
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        padding = to_2tuple(padding)
+        dilation = to_2tuple(dilation)
+
+        self.padding = padding
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.dilation = dilation
+
+    def get_pad_shape(self, input_shape):
+        input_h, input_w = input_shape
+        kernel_h, kernel_w = self.kernel_size
+        stride_h, stride_w = self.stride
+        output_h = math.ceil(input_h / stride_h)
+        output_w = math.ceil(input_w / stride_w)
+        pad_h = max((output_h - 1) * stride_h + (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
+        pad_w = max((output_w - 1) * stride_w + (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
+        return pad_h, pad_w
+
+    def forward(self, x):
+        pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
+        if pad_h > 0 or pad_w > 0:
+            if self.padding == "corner":
+                x = F.pad(x, [0, pad_w, 0, pad_h])
+            elif self.padding == "same":
+                x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
+        return x
+
+
+class PatchMerging(BaseModule):
+    """Merge patch feature map.
+
+    This layer groups feature map by kernel_size, and applies norm and linear
+    layers to the grouped feature map. Our implementation uses `nn.Unfold` to
+    merge patch, which is about 25% faster than original implementation.
+    Instead, we need to modify pretrained models for compatibility.
+
+    Args:
+        in_channels (int): The num of input channels.
+            to gets fully covered by filter and stride you specified..
+            Default: True.
+        out_channels (int): The num of output channels.
+        kernel_size (int | tuple, optional): the kernel size in the unfold
+            layer. Defaults to 2.
+        stride (int | tuple, optional): the stride of the sliding blocks in the
+            unfold layer. Default: None. (Would be set as `kernel_size`)
+        padding (int | tuple | string ): The padding length of
+            embedding conv. When it is a string, it means the mode
+            of adaptive padding, support "same" and "corner" now.
+            Default: "corner".
+        dilation (int | tuple, optional): dilation parameter in the unfold
+            layer. Default: 1.
+        bias (bool, optional): Whether to add bias in linear layer or not.
+            Defaults: False.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: dict(type='LN').
+        init_cfg (dict, optional): The extra config for initialization.
+            Default: None.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=2,
+        stride=None,
+        padding="corner",
+        dilation=1,
+        bias=False,
+        norm_cfg=dict(type="LN"),
+        init_cfg=None,
+    ):
+        super().__init__(init_cfg=init_cfg)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        if stride:
+            stride = stride
+        else:
+            stride = kernel_size
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        if isinstance(padding, str):
+            self.adap_padding = AdaptivePadding(
+                kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding
+            )
+            # disable the padding of unfold
+            padding = 0
+        else:
+            self.adap_padding = None
+
+        padding = to_2tuple(padding)
+        self.sampler = nn.Unfold(kernel_size=kernel_size, dilation=dilation, padding=padding, stride=stride)
+
+        sample_dim = kernel_size[0] * kernel_size[1] * in_channels
+
+        if norm_cfg is not None:
+            self.norm = build_norm_layer(norm_cfg, sample_dim)[1]
+        else:
+            self.norm = None
+
+        self.reduction = nn.Linear(sample_dim, out_channels, bias=bias)
+
+    def forward(self, x, input_size):
+        """
+        Args:
+            x (Tensor): Has shape (B, H*W, C_in).
+            input_size (tuple[int]): The spatial shape of x, arrange as (H, W).
+                Default: None.
+
+        Returns:
+            tuple: Contains merged results and its spatial shape.
+
+                - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out)
+                - out_size (tuple[int]): Spatial shape of x, arrange as
+                    (Merged_H, Merged_W).
+        """
+        B, L, C = x.shape
+        assert isinstance(input_size, Sequence), f"Expect " f"input_size is " f"`Sequence` " f"but get {input_size}"
+
+        H, W = input_size
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C).permute([0, 3, 1, 2])  # B, C, H, W
+        # Use nn.Unfold to merge patch. About 25% faster than original method,
+        # but need to modify pretrained model for compatibility
+
+        if self.adap_padding:
+            x = self.adap_padding(x)
+            H, W = x.shape[-2:]
+
+        x = self.sampler(x)
+        # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2)
+
+        out_h = (
+            H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] * (self.sampler.kernel_size[0] - 1) - 1
+        ) // self.sampler.stride[0] + 1
+        out_w = (
+            W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] * (self.sampler.kernel_size[1] - 1) - 1
+        ) // self.sampler.stride[1] + 1
+
+        output_size = (out_h, out_w)
+        x = x.transpose(1, 2)  # B, H/2*W/2, 4*C
+        x = self.norm(x) if self.norm else x
+        x = self.reduction(x)
+        return x, output_size
+
+
+def inverse_sigmoid(x, eps=1e-5):
+    """Inverse function of sigmoid.
+
+    Args:
+        x (Tensor): The tensor to do the
+            inverse.
+        eps (float): EPS avoid numerical
+            overflow. Defaults 1e-5.
+    Returns:
+        Tensor: The x has passed the inverse
+            function of sigmoid, has same
+            shape with input.
+    """
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+@FEEDFORWARD_NETWORK.register_module(force=True)
+class FFN(BaseModule):
+    """Implements feed-forward networks (FFNs) with identity connection.
+    Args:
+        embed_dims (int): The feature dimension. Same as
+            `MultiheadAttention`. Defaults: 256.
+        feedforward_channels (int): The hidden dimension of FFNs.
+            Defaults: 1024.
+        num_fcs (int, optional): The number of fully-connected layers in
+            FFNs. Default: 2.
+        act_cfg (dict, optional): The activation config for FFNs.
+            Default: dict(type='ReLU')
+        ffn_drop (float, optional): Probability of an element to be
+            zeroed in FFN. Default 0.0.
+        add_identity (bool, optional): Whether to add the
+            identity connection. Default: `True`.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+    """
+
+    @deprecated_api_warning({"dropout": "ffn_drop", "add_residual": "add_identity"}, cls_name="FFN")
+    def __init__(
+        self,
+        embed_dims=256,
+        feedforward_channels=1024,
+        num_fcs=2,
+        act_cfg=dict(type="ReLU", inplace=True),
+        ffn_drop=0.0,
+        dropout_layer=None,
+        add_identity=True,
+        init_cfg=None,
+        with_cp=False,
+        **kwargs,
+    ):
+        super().__init__(init_cfg)
+        assert num_fcs >= 2, "num_fcs should be no less " f"than 2. got {num_fcs}."
+        self.embed_dims = embed_dims
+        self.feedforward_channels = feedforward_channels
+        self.num_fcs = num_fcs
+        self.act_cfg = act_cfg
+        self.activate = build_activation_layer(act_cfg)
+        self.with_cp = with_cp
+        layers = []
+        in_channels = embed_dims
+        for _ in range(num_fcs - 1):
+            layers.append(Sequential(Linear(in_channels, feedforward_channels), self.activate, nn.Dropout(ffn_drop)))
+            in_channels = feedforward_channels
+        layers.append(Linear(feedforward_channels, embed_dims))
+        layers.append(nn.Dropout(ffn_drop))
+        self.layers = Sequential(*layers)
+        self.dropout_layer = build_dropout(dropout_layer) if dropout_layer else torch.nn.Identity()
+        self.add_identity = add_identity
+
+    @deprecated_api_warning({"residual": "identity"}, cls_name="FFN")
+    def forward(self, x, identity=None):
+        """Forward function for `FFN`.
+        The function would add x to the output tensor if residue is None.
+        """
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(self.layers, x)
+        else:
+            out = self.layers(x)
+
+        if not self.add_identity:
+            return self.dropout_layer(out)
+        if identity is None:
+            identity = x
+        return identity + self.dropout_layer(out)
+
+
+@TRANSFORMER_LAYER.register_module()
+class DetrTransformerDecoderLayer(BaseTransformerLayer):
+    """Implements decoder layer in DETR transformer.
+
+    Args:
+        attn_cfgs (list[`mmcv.ConfigDict`] | list[dict] | dict )):
+            Configs for self_attention or cross_attention, the order
+            should be consistent with it in `operation_order`. If it is
+            a dict, it would be expand to the number of attention in
+            `operation_order`.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        ffn_dropout (float): Probability of an element to be zeroed
+            in ffn. Default 0.0.
+        operation_order (tuple[str]): The execution order of operation
+            in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm').
+            Default：None
+        act_cfg (dict): The activation config for FFNs. Default: `LN`
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: `LN`.
+        ffn_num_fcs (int): The number of fully-connected layers in FFNs.
+            Default：2.
+    """
+
+    def __init__(
+        self,
+        attn_cfgs,
+        feedforward_channels,
+        ffn_dropout=0.0,
+        operation_order=None,
+        act_cfg=dict(type="ReLU", inplace=True),
+        norm_cfg=dict(type="LN"),
+        ffn_num_fcs=2,
+        **kwargs,
+    ):
+        super(DetrTransformerDecoderLayer, self).__init__(
+            attn_cfgs=attn_cfgs,
+            feedforward_channels=feedforward_channels,
+            ffn_dropout=ffn_dropout,
+            operation_order=operation_order,
+            act_cfg=act_cfg,
+            norm_cfg=norm_cfg,
+            ffn_num_fcs=ffn_num_fcs,
+            **kwargs,
+        )
+        assert len(operation_order) == 6
+        assert set(operation_order) == set(["self_attn", "norm", "cross_attn", "ffn"])
+
+
+@TRANSFORMER_LAYER_SEQUENCE.register_module()
+class DetrTransformerEncoder(TransformerLayerSequence):
+    """TransformerEncoder of DETR.
+
+    Args:
+        post_norm_cfg (dict): Config of last normalization layer. Default：
+            `LN`. Only used when `self.pre_norm` is `True`
+    """
+
+    def __init__(self, *args, post_norm_cfg=dict(type="LN"), **kwargs):
+        super(DetrTransformerEncoder, self).__init__(*args, **kwargs)
+        if post_norm_cfg is not None:
+            self.post_norm = build_norm_layer(post_norm_cfg, self.embed_dims)[1] if self.pre_norm else None
+        else:
+            assert not self.pre_norm, f"Use prenorm in " f"{self.__class__.__name__}," f"Please specify post_norm_cfg"
+            self.post_norm = None
+
+    def forward(self, *args, **kwargs):
+        """Forward function for `TransformerCoder`.
+
+        Returns:
+            Tensor: forwarded results with shape [num_query, bs, embed_dims].
+        """
+        x = super(DetrTransformerEncoder, self).forward(*args, **kwargs)
+        if self.post_norm is not None:
+            x = self.post_norm(x)
+        return x
+
+
+@TRANSFORMER_LAYER_SEQUENCE.register_module()
+class DetrTransformerDecoder(TransformerLayerSequence):
+    """Implements the decoder in DETR transformer.
+
+    Args:
+        return_intermediate (bool): Whether to return intermediate outputs.
+        post_norm_cfg (dict): Config of last normalization layer. Default：
+            `LN`.
+    """
+
+    def __init__(self, *args, post_norm_cfg=dict(type="LN"), return_intermediate=False, **kwargs):
+
+        super(DetrTransformerDecoder, self).__init__(*args, **kwargs)
+        self.return_intermediate = return_intermediate
+        if post_norm_cfg is not None:
+            self.post_norm = build_norm_layer(post_norm_cfg, self.embed_dims)[1]
+        else:
+            self.post_norm = None
+
+    def forward(self, query, *args, **kwargs):
+        """Forward function for `TransformerDecoder`.
+
+        Args:
+            query (Tensor): Input query with shape
+                `(num_query, bs, embed_dims)`.
+
+        Returns:
+            Tensor: Results with shape [1, num_query, bs, embed_dims] when
+                return_intermediate is `False`, otherwise it has shape
+                [num_layers, num_query, bs, embed_dims].
+        """
+        if not self.return_intermediate:
+            x = super().forward(query, *args, **kwargs)
+            if self.post_norm:
+                x = self.post_norm(x)[None]
+            return x
+
+        intermediate = []
+        for layer in self.layers:
+            query = layer(query, *args, **kwargs)
+            if self.return_intermediate:
+                if self.post_norm is not None:
+                    intermediate.append(self.post_norm(query))
+                else:
+                    intermediate.append(query)
+        return torch.stack(intermediate)
+
+
+@TRANSFORMER.register_module()
+class Transformer(BaseModule):
+    """Implements the DETR transformer.
+
+    Following the official DETR implementation, this module copy-paste
+    from torch.nn.Transformer with modifications:
+
+        * positional encodings are passed in MultiheadAttention
+        * extra LN at the end of encoder is removed
+        * decoder returns a stack of activations from all decoding layers
+
+    See `paper: End-to-End Object Detection with Transformers
+    <https://arxiv.org/pdf/2005.12872>`_ for details.
+
+    Args:
+        encoder (`mmcv.ConfigDict` | Dict): Config of
+            TransformerEncoder. Defaults to None.
+        decoder ((`mmcv.ConfigDict` | Dict)): Config of
+            TransformerDecoder. Defaults to None
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Defaults to None.
+    """
+
+    def __init__(self, encoder=None, decoder=None, init_cfg=None):
+        super(Transformer, self).__init__(init_cfg=init_cfg)
+        self.encoder = build_transformer_layer_sequence(encoder)
+        self.decoder = build_transformer_layer_sequence(decoder)
+        self.embed_dims = self.encoder.embed_dims
+
+    def init_weights(self):
+        # follow the official DETR to init parameters
+        for m in self.modules():
+            if hasattr(m, "weight") and m.weight.dim() > 1:
+                xavier_init(m, distribution="uniform")
+        self._is_init = True
+
+    def forward(self, x, mask, query_embed, pos_embed):
+        """Forward function for `Transformer`.
+
+        Args:
+            x (Tensor): Input query with shape [bs, c, h, w] where
+                c = embed_dims.
+            mask (Tensor): The key_padding_mask used for encoder and decoder,
+                with shape [bs, h, w].
+            query_embed (Tensor): The query embedding for decoder, with shape
+                [num_query, c].
+            pos_embed (Tensor): The positional encoding for encoder and
+                decoder, with the same shape as `x`.
+
+        Returns:
+            tuple[Tensor]: results of decoder containing the following tensor.
+
+                - out_dec: Output from decoder. If return_intermediate_dec \
+                      is True output has shape [num_dec_layers, bs,
+                      num_query, embed_dims], else has shape [1, bs, \
+                      num_query, embed_dims].
+                - memory: Output results from encoder, with shape \
+                      [bs, embed_dims, h, w].
+        """
+        bs, c, h, w = x.shape
+        # use `view` instead of `flatten` for dynamically exporting to ONNX
+        x = x.view(bs, c, -1).permute(2, 0, 1)  # [bs, c, h, w] -> [h*w, bs, c]
+        pos_embed = pos_embed.view(bs, c, -1).permute(2, 0, 1)
+        query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)  # [num_query, dim] -> [num_query, bs, dim]
+        mask = mask.view(bs, -1)  # [bs, h, w] -> [bs, h*w]
+        memory = self.encoder(query=x, key=None, value=None, query_pos=pos_embed, query_key_padding_mask=mask)
+        target = torch.zeros_like(query_embed)
+        # out_dec: [num_layers, num_query, bs, dim]
+        out_dec = self.decoder(
+            query=target, key=memory, value=memory, key_pos=pos_embed, query_pos=query_embed, key_padding_mask=mask
+        )
+        out_dec = out_dec.transpose(1, 2)
+        memory = memory.permute(1, 2, 0).reshape(bs, c, h, w)
+        return out_dec, memory
+
+
+@TRANSFORMER_LAYER_SEQUENCE.register_module()
+class DeformableDetrTransformerDecoder(TransformerLayerSequence):
+    """Implements the decoder in DETR transformer.
+
+    Args:
+        return_intermediate (bool): Whether to return intermediate outputs.
+        coder_norm_cfg (dict): Config of last normalization layer. Default：
+            `LN`.
+    """
+
+    def __init__(self, *args, return_intermediate=False, **kwargs):
+
+        super(DeformableDetrTransformerDecoder, self).__init__(*args, **kwargs)
+        self.return_intermediate = return_intermediate
+
+    def forward(self, query, *args, reference_points=None, valid_ratios=None, reg_branches=None, **kwargs):
+        """Forward function for `TransformerDecoder`.
+
+        Args:
+            query (Tensor): Input query with shape
+                `(num_query, bs, embed_dims)`.
+            reference_points (Tensor): The reference
+                points of offset. has shape
+                (bs, num_query, 4) when as_two_stage,
+                otherwise has shape ((bs, num_query, 2).
+            valid_ratios (Tensor): The radios of valid
+                points on the feature map, has shape
+                (bs, num_levels, 2)
+            reg_branch: (obj:`nn.ModuleList`): Used for
+                refining the regression results. Only would
+                be passed when with_box_refine is True,
+                otherwise would be passed a `None`.
+
+        Returns:
+            Tensor: Results with shape [1, num_query, bs, embed_dims] when
+                return_intermediate is `False`, otherwise it has shape
+                [num_layers, num_query, bs, embed_dims].
+        """
+        output = query
+        intermediate = []
+        intermediate_reference_points = []
+        for lid, layer in enumerate(self.layers):
+            if reference_points.shape[-1] == 4:
+                reference_points_input = (
+                    reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
+                )
+            else:
+                assert reference_points.shape[-1] == 2
+                reference_points_input = reference_points[:, :, None] * valid_ratios[:, None]
+            output = layer(output, *args, reference_points=reference_points_input, **kwargs)
+            output = output.permute(1, 0, 2)
+
+            if reg_branches is not None:
+                tmp = reg_branches[lid](output)
+                if reference_points.shape[-1] == 4:
+                    new_reference_points = tmp + inverse_sigmoid(reference_points)
+                    new_reference_points = new_reference_points.sigmoid()
+                else:
+                    assert reference_points.shape[-1] == 2
+                    new_reference_points = tmp
+                    new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)
+                    new_reference_points = new_reference_points.sigmoid()
+                reference_points = new_reference_points.detach()
+
+            output = output.permute(1, 0, 2)
+            if self.return_intermediate:
+                intermediate.append(output)
+                intermediate_reference_points.append(reference_points)
+
+        if self.return_intermediate:
+            return torch.stack(intermediate), torch.stack(intermediate_reference_points)
+
+        return output, reference_points
+
+
+@TRANSFORMER.register_module()
+class DeformableDetrTransformer(Transformer):
+    """Implements the DeformableDETR transformer.
+
+    Args:
+        as_two_stage (bool): Generate query from encoder features.
+            Default: False.
+        num_feature_levels (int): Number of feature maps from FPN:
+            Default: 4.
+        two_stage_num_proposals (int): Number of proposals when set
+            `as_two_stage` as True. Default: 300.
+    """
+
+    def __init__(self, as_two_stage=False, num_feature_levels=4, two_stage_num_proposals=300, **kwargs):
+        super(DeformableDetrTransformer, self).__init__(**kwargs)
+        self.as_two_stage = as_two_stage
+        self.num_feature_levels = num_feature_levels
+        self.two_stage_num_proposals = two_stage_num_proposals
+        self.embed_dims = self.encoder.embed_dims
+        self.init_layers()
+
+    def init_layers(self):
+        """Initialize layers of the DeformableDetrTransformer."""
+        self.level_embeds = nn.Parameter(torch.Tensor(self.num_feature_levels, self.embed_dims))
+
+        if self.as_two_stage:
+            self.enc_output = nn.Linear(self.embed_dims, self.embed_dims)
+            self.enc_output_norm = nn.LayerNorm(self.embed_dims)
+            self.pos_trans = nn.Linear(self.embed_dims * 2, self.embed_dims * 2)
+            self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2)
+        else:
+            self.reference_points = nn.Linear(self.embed_dims, 2)
+
+    def init_weights(self):
+        """Initialize the transformer weights."""
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        for m in self.modules():
+            if isinstance(m, MultiScaleDeformableAttention):
+                m.init_weights()
+        if not self.as_two_stage:
+            xavier_init(self.reference_points, distribution="uniform", bias=0.0)
+        normal_(self.level_embeds)
+
+    def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes):
+        """Generate proposals from encoded memory.
+
+        Args:
+            memory (Tensor) : The output of encoder,
+                has shape (bs, num_key, embed_dim).  num_key is
+                equal the number of points on feature map from
+                all level.
+            memory_padding_mask (Tensor): Padding mask for memory.
+                has shape (bs, num_key).
+            spatial_shapes (Tensor): The shape of all feature maps.
+                has shape (num_level, 2).
+
+        Returns:
+            tuple: A tuple of feature map and bbox prediction.
+
+                - output_memory (Tensor): The input of decoder,  \
+                    has shape (bs, num_key, embed_dim).  num_key is \
+                    equal the number of points on feature map from \
+                    all levels.
+                - output_proposals (Tensor): The normalized proposal \
+                    after a inverse sigmoid, has shape \
+                    (bs, num_keys, 4).
+        """
+
+        N, S, C = memory.shape
+        proposals = []
+        _cur = 0
+        for lvl, (H, W) in enumerate(spatial_shapes):
+            mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H * W)].view(N, H, W, 1)
+            valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
+            valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
+
+            grid_y, grid_x = torch.meshgrid(
+                torch.linspace(0, H - 1, H, dtype=torch.float32, device=memory.device),
+                torch.linspace(0, W - 1, W, dtype=torch.float32, device=memory.device),
+            )
+            grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
+
+            scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2)
+            grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale
+            wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
+            proposal = torch.cat((grid, wh), -1).view(N, -1, 4)
+            proposals.append(proposal)
+            _cur += H * W
+        output_proposals = torch.cat(proposals, 1)
+        output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
+        output_proposals = torch.log(output_proposals / (1 - output_proposals))
+        output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
+        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
+
+        output_memory = memory
+        output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
+        output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
+        output_memory = self.enc_output_norm(self.enc_output(output_memory))
+        return output_memory, output_proposals
+
+    @staticmethod
+    def get_reference_points(spatial_shapes, valid_ratios, device):
+        """Get the reference points used in decoder.
+
+        Args:
+            spatial_shapes (Tensor): The shape of all
+                feature maps, has shape (num_level, 2).
+            valid_ratios (Tensor): The radios of valid
+                points on the feature map, has shape
+                (bs, num_levels, 2)
+            device (obj:`device`): The device where
+                reference_points should be.
+
+        Returns:
+            Tensor: reference points used in decoder, has \
+                shape (bs, num_keys, num_levels, 2).
+        """
+        reference_points_list = []
+        for lvl, (H, W) in enumerate(spatial_shapes):
+            ref_y, ref_x = torch.meshgrid(
+                torch.linspace(0.5, H - 0.5, H, dtype=torch.float32, device=device),
+                torch.linspace(0.5, W - 0.5, W, dtype=torch.float32, device=device),
+            )
+            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H)
+            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W)
+            ref = torch.stack((ref_x, ref_y), -1)
+            reference_points_list.append(ref)
+        reference_points = torch.cat(reference_points_list, 1)
+        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+        return reference_points
+
+    def get_valid_ratio(self, mask):
+        """Get the valid radios of feature maps of all  level."""
+        _, H, W = mask.shape
+        valid_H = torch.sum(~mask[:, :, 0], 1)
+        valid_W = torch.sum(~mask[:, 0, :], 1)
+        valid_ratio_h = valid_H.float() / H
+        valid_ratio_w = valid_W.float() / W
+        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+        return valid_ratio
+
+    def get_proposal_pos_embed(self, proposals, num_pos_feats=128, temperature=10000):
+        """Get the position embedding of proposal."""
+        scale = 2 * math.pi
+        dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
+        dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
+        # N, L, 4
+        proposals = proposals.sigmoid() * scale
+        # N, L, 4, 128
+        pos = proposals[:, :, :, None] / dim_t
+        # N, L, 4, 64, 2
+        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
+        return pos
+
+    def forward(
+        self, mlvl_feats, mlvl_masks, query_embed, mlvl_pos_embeds, reg_branches=None, cls_branches=None, **kwargs
+    ):
+        """Forward function for `Transformer`.
+
+        Args:
+            mlvl_feats (list(Tensor)): Input queries from
+                different level. Each element has shape
+                [bs, embed_dims, h, w].
+            mlvl_masks (list(Tensor)): The key_padding_mask from
+                different level used for encoder and decoder,
+                each element has shape  [bs, h, w].
+            query_embed (Tensor): The query embedding for decoder,
+                with shape [num_query, c].
+            mlvl_pos_embeds (list(Tensor)): The positional encoding
+                of feats from different level, has the shape
+                 [bs, embed_dims, h, w].
+            reg_branches (obj:`nn.ModuleList`): Regression heads for
+                feature maps from each decoder layer. Only would
+                be passed when
+                `with_box_refine` is True. Default to None.
+            cls_branches (obj:`nn.ModuleList`): Classification heads
+                for feature maps from each decoder layer. Only would
+                 be passed when `as_two_stage`
+                 is True. Default to None.
+
+
+        Returns:
+            tuple[Tensor]: results of decoder containing the following tensor.
+
+                - inter_states: Outputs from decoder. If
+                    return_intermediate_dec is True output has shape \
+                      (num_dec_layers, bs, num_query, embed_dims), else has \
+                      shape (1, bs, num_query, embed_dims).
+                - init_reference_out: The initial value of reference \
+                    points, has shape (bs, num_queries, 4).
+                - inter_references_out: The internal value of reference \
+                    points in decoder, has shape \
+                    (num_dec_layers, bs,num_query, embed_dims)
+                - enc_outputs_class: The classification score of \
+                    proposals generated from \
+                    encoder's feature maps, has shape \
+                    (batch, h*w, num_classes). \
+                    Only would be returned when `as_two_stage` is True, \
+                    otherwise None.
+                - enc_outputs_coord_unact: The regression results \
+                    generated from encoder's feature maps., has shape \
+                    (batch, h*w, 4). Only would \
+                    be returned when `as_two_stage` is True, \
+                    otherwise None.
+        """
+        assert self.as_two_stage or query_embed is not None
+
+        feat_flatten = []
+        mask_flatten = []
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        for lvl, (feat, mask, pos_embed) in enumerate(zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)):
+            bs, c, h, w = feat.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+            feat = feat.flatten(2).transpose(1, 2)
+            mask = mask.flatten(1)
+            pos_embed = pos_embed.flatten(2).transpose(1, 2)
+            lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1)
+            lvl_pos_embed_flatten.append(lvl_pos_embed)
+            feat_flatten.append(feat)
+            mask_flatten.append(mask)
+        feat_flatten = torch.cat(feat_flatten, 1)
+        mask_flatten = torch.cat(mask_flatten, 1)
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
+        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=feat_flatten.device)
+        level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
+        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in mlvl_masks], 1)
+
+        reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=feat.device)
+
+        feat_flatten = feat_flatten.permute(1, 0, 2)  # (H*W, bs, embed_dims)
+        lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute(1, 0, 2)  # (H*W, bs, embed_dims)
+        memory = self.encoder(
+            query=feat_flatten,
+            key=None,
+            value=None,
+            query_pos=lvl_pos_embed_flatten,
+            query_key_padding_mask=mask_flatten,
+            spatial_shapes=spatial_shapes,
+            reference_points=reference_points,
+            level_start_index=level_start_index,
+            valid_ratios=valid_ratios,
+            **kwargs,
+        )
+
+        memory = memory.permute(1, 0, 2)
+        bs, _, c = memory.shape
+        if self.as_two_stage:
+            output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes)
+            enc_outputs_class = cls_branches[self.decoder.num_layers](output_memory)
+            enc_outputs_coord_unact = reg_branches[self.decoder.num_layers](output_memory) + output_proposals
+
+            topk = self.two_stage_num_proposals
+            topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]
+            topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4))
+            topk_coords_unact = topk_coords_unact.detach()
+            reference_points = topk_coords_unact.sigmoid()
+            init_reference_out = reference_points
+            pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))
+            query_pos, query = torch.split(pos_trans_out, c, dim=2)
+        else:
+            query_pos, query = torch.split(query_embed, c, dim=1)
+            query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1)
+            query = query.unsqueeze(0).expand(bs, -1, -1)
+            reference_points = self.reference_points(query_pos).sigmoid()
+            init_reference_out = reference_points
+
+        # decoder
+        query = query.permute(1, 0, 2)
+        memory = memory.permute(1, 0, 2)
+        query_pos = query_pos.permute(1, 0, 2)
+        inter_states, inter_references = self.decoder(
+            query=query,
+            key=None,
+            value=memory,
+            query_pos=query_pos,
+            key_padding_mask=mask_flatten,
+            reference_points=reference_points,
+            spatial_shapes=spatial_shapes,
+            level_start_index=level_start_index,
+            valid_ratios=valid_ratios,
+            reg_branches=reg_branches,
+            **kwargs,
+        )
+
+        inter_references_out = inter_references
+        if self.as_two_stage:
+            return inter_states, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact
+        return inter_states, init_reference_out, inter_references_out, None, None
+
+
+@TRANSFORMER.register_module()
+class DynamicConv(BaseModule):
+    """Implements Dynamic Convolution.
+
+    This module generate parameters for each sample and
+    use bmm to implement 1*1 convolution. Code is modified
+    from the `official github repo <https://github.com/PeizeSun/
+    SparseR-CNN/blob/main/projects/SparseRCNN/sparsercnn/head.py#L258>`_ .
+
+    Args:
+        in_channels (int): The input feature channel.
+            Defaults to 256.
+        feat_channels (int): The inner feature channel.
+            Defaults to 64.
+        out_channels (int, optional): The output feature channel.
+            When not specified, it will be set to `in_channels`
+            by default
+        input_feat_shape (int): The shape of input feature.
+            Defaults to 7.
+        with_proj (bool): Project two-dimentional feature to
+            one-dimentional feature. Default to True.
+        act_cfg (dict): The activation config for DynamicConv.
+        norm_cfg (dict): Config dict for normalization layer. Default
+            layer normalization.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(
+        self,
+        in_channels=256,
+        feat_channels=64,
+        out_channels=None,
+        input_feat_shape=7,
+        with_proj=True,
+        act_cfg=dict(type="ReLU", inplace=True),
+        norm_cfg=dict(type="LN"),
+        init_cfg=None,
+    ):
+        super(DynamicConv, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.feat_channels = feat_channels
+        self.out_channels_raw = out_channels
+        self.input_feat_shape = input_feat_shape
+        self.with_proj = with_proj
+        self.act_cfg = act_cfg
+        self.norm_cfg = norm_cfg
+        self.out_channels = out_channels if out_channels else in_channels
+
+        self.num_params_in = self.in_channels * self.feat_channels
+        self.num_params_out = self.out_channels * self.feat_channels
+        self.dynamic_layer = nn.Linear(self.in_channels, self.num_params_in + self.num_params_out)
+
+        self.norm_in = build_norm_layer(norm_cfg, self.feat_channels)[1]
+        self.norm_out = build_norm_layer(norm_cfg, self.out_channels)[1]
+
+        self.activation = build_activation_layer(act_cfg)
+
+        num_output = self.out_channels * input_feat_shape**2
+        if self.with_proj:
+            self.fc_layer = nn.Linear(num_output, self.out_channels)
+            self.fc_norm = build_norm_layer(norm_cfg, self.out_channels)[1]
+
+    def forward(self, param_feature, input_feature):
+        """Forward function for `DynamicConv`.
+
+        Args:
+            param_feature (Tensor): The feature can be used
+                to generate the parameter, has shape
+                (num_all_proposals, in_channels).
+            input_feature (Tensor): Feature that
+                interact with parameters, has shape
+                (num_all_proposals, in_channels, H, W).
+
+        Returns:
+            Tensor: The output feature has shape
+            (num_all_proposals, out_channels).
+        """
+        input_feature = input_feature.flatten(2).permute(2, 0, 1)
+
+        input_feature = input_feature.permute(1, 0, 2)
+        parameters = self.dynamic_layer(param_feature)
+
+        param_in = parameters[:, : self.num_params_in].view(-1, self.in_channels, self.feat_channels)
+        param_out = parameters[:, -self.num_params_out :].view(-1, self.feat_channels, self.out_channels)
+
+        # input_feature has shape (num_all_proposals, H*W, in_channels)
+        # param_in has shape (num_all_proposals, in_channels, feat_channels)
+        # feature has shape (num_all_proposals, H*W, feat_channels)
+        features = torch.bmm(input_feature, param_in)
+        features = self.norm_in(features)
+        features = self.activation(features)
+
+        # param_out has shape (batch_size, feat_channels, out_channels)
+        features = torch.bmm(features, param_out)
+        features = self.norm_out(features)
+        features = self.activation(features)
+
+        if self.with_proj:
+            features = features.flatten(1)
+            features = self.fc_layer(features)
+            features = self.fc_norm(features)
+            features = self.activation(features)
+
+        return features
diff --git a/src/dinov2/eval/segmentation_m2f/ops/modules/__init__.py b/src/dinov2/eval/segmentation_m2f/ops/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..49aa8fe612fd4c088e294707c5ee16bd1cb5b5e7
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/ops/modules/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/fundamentalvision/Deformable-DETR/tree/main/models/ops/modules
+#   https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+
+from .ms_deform_attn import MSDeformAttn
diff --git a/src/dinov2/eval/segmentation_m2f/ops/modules/ms_deform_attn.py b/src/dinov2/eval/segmentation_m2f/ops/modules/ms_deform_attn.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8b4fa23712e87d1a2682b57e71ee37fe8524cff
--- /dev/null
+++ b/src/dinov2/eval/segmentation_m2f/ops/modules/ms_deform_attn.py
@@ -0,0 +1,185 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import math
+import warnings
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.autograd import Function
+from torch.cuda.amp import custom_fwd
+from torch.nn.init import constant_, xavier_uniform_
+
+
+class MSDeformAttnFunction(Function):
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float32)
+    def forward(
+        ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step
+    ):
+        output = ms_deform_attn_core_pytorch(
+            value,
+            value_spatial_shapes,
+            #  value_level_start_index,
+            sampling_locations,
+            attention_weights,
+        )
+        return output
+
+
+def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):
+    # for debug and test only,
+    # need to use cuda version instead
+    N_, S_, M_, D_ = value.shape
+    _, Lq_, M_, L_, P_, _ = sampling_locations.shape
+    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
+    sampling_grids = 2 * sampling_locations - 1
+    sampling_value_list = []
+    for lid_, (H_, W_) in enumerate(value_spatial_shapes):
+        # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
+        value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_ * M_, D_, H_, W_)
+        # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
+        sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)
+        # N_*M_, D_, Lq_, P_
+        sampling_value_l_ = F.grid_sample(
+            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+        )
+        sampling_value_list.append(sampling_value_l_)
+    # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)
+    attention_weights = attention_weights.transpose(1, 2).reshape(N_ * M_, 1, Lq_, L_ * P_)
+    output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_ * D_, Lq_)
+    return output.transpose(1, 2).contiguous()
+
+
+def _is_power_of_2(n):
+    if (not isinstance(n, int)) or (n < 0):
+        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+    return (n & (n - 1) == 0) and n != 0
+
+
+class MSDeformAttn(nn.Module):
+    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, ratio=1.0):
+        """Multi-Scale Deformable Attention Module.
+
+        :param d_model      hidden dimension
+        :param n_levels     number of feature levels
+        :param n_heads      number of attention heads
+        :param n_points     number of sampling points per attention head per feature level
+        """
+        super().__init__()
+        if d_model % n_heads != 0:
+            raise ValueError("d_model must be divisible by n_heads, " "but got {} and {}".format(d_model, n_heads))
+        _d_per_head = d_model // n_heads
+        # you'd better set _d_per_head to a power of 2
+        # which is more efficient in our CUDA implementation
+        if not _is_power_of_2(_d_per_head):
+            warnings.warn(
+                "You'd better set d_model in MSDeformAttn to make "
+                "the dimension of each attention head a power of 2 "
+                "which is more efficient in our CUDA implementation."
+            )
+
+        self.im2col_step = 64
+
+        self.d_model = d_model
+        self.n_levels = n_levels
+        self.n_heads = n_heads
+        self.n_points = n_points
+        self.ratio = ratio
+        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
+        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
+        self.value_proj = nn.Linear(d_model, int(d_model * ratio))
+        self.output_proj = nn.Linear(int(d_model * ratio), d_model)
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        constant_(self.sampling_offsets.weight.data, 0.0)
+        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
+        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+        grid_init = (
+            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+            .view(self.n_heads, 1, 1, 2)
+            .repeat(1, self.n_levels, self.n_points, 1)
+        )
+        for i in range(self.n_points):
+            grid_init[:, :, i, :] *= i + 1
+
+        with torch.no_grad():
+            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+        constant_(self.attention_weights.weight.data, 0.0)
+        constant_(self.attention_weights.bias.data, 0.0)
+        xavier_uniform_(self.value_proj.weight.data)
+        constant_(self.value_proj.bias.data, 0.0)
+        xavier_uniform_(self.output_proj.weight.data)
+        constant_(self.output_proj.bias.data, 0.0)
+
+    def forward(
+        self,
+        query,
+        reference_points,
+        input_flatten,
+        input_spatial_shapes,
+        input_level_start_index,
+        input_padding_mask=None,
+    ):
+        """
+        :param query                       (N, Length_{query}, C)
+        :param reference_points            (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area
+                                        or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes
+        :param input_flatten               (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C)
+        :param input_spatial_shapes        (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
+        :param input_level_start_index     (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]
+        :param input_padding_mask          (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements
+
+        :return output                     (N, Length_{query}, C)
+        """
+        # print(query.shape)
+        # print(reference_points.shape)
+        # print(input_flatten.shape)
+        # print(input_spatial_shapes.shape)
+        # print(input_level_start_index.shape)
+        # print(input_spatial_shapes)
+        # print(input_level_start_index)
+
+        N, Len_q, _ = query.shape
+        N, Len_in, _ = input_flatten.shape
+        assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in
+
+        value = self.value_proj(input_flatten)
+        if input_padding_mask is not None:
+            value = value.masked_fill(input_padding_mask[..., None], float(0))
+
+        value = value.view(N, Len_in, self.n_heads, int(self.ratio * self.d_model) // self.n_heads)
+        sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)
+        attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
+        attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
+
+        if reference_points.shape[-1] == 2:
+            offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)
+            sampling_locations = (
+                reference_points[:, :, None, :, None, :]
+                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+            )
+        elif reference_points.shape[-1] == 4:
+            sampling_locations = (
+                reference_points[:, :, None, :, None, :2]
+                + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
+            )
+        else:
+            raise ValueError(
+                "Last dim of reference_points must be 2 or 4, but get {} instead.".format(reference_points.shape[-1])
+            )
+        output = MSDeformAttnFunction.apply(
+            value,
+            input_spatial_shapes,
+            input_level_start_index,
+            sampling_locations,
+            attention_weights,
+            self.im2col_step,
+        )
+        output = self.output_proj(output)
+        return output
diff --git a/src/dinov2/eval/setup.py b/src/dinov2/eval/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..959128c0673cc51036dbf17dcc4ee68a037988fb
--- /dev/null
+++ b/src/dinov2/eval/setup.py
@@ -0,0 +1,75 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import argparse
+from typing import Any, List, Optional, Tuple
+
+import torch
+import torch.backends.cudnn as cudnn
+
+from dinov2.models import build_model_from_cfg
+from dinov2.utils.config import setup
+import dinov2.utils.utils as dinov2_utils
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+):
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents or [],
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--config-file",
+        type=str,
+        help="Model configuration file",
+    )
+    parser.add_argument(
+        "--pretrained-weights",
+        type=str,
+        help="Pretrained model weights",
+    )
+    parser.add_argument(
+        "--output-dir",
+        default="",
+        type=str,
+        help="Output directory to write results and logs",
+    )
+    parser.add_argument(
+        "--opts",
+        help="Extra configuration options",
+        default=[],
+        nargs="+",
+    )
+    return parser
+
+
+def get_autocast_dtype(config):
+    teacher_dtype_str = config.compute_precision.teacher.backbone.mixed_precision.param_dtype
+    if teacher_dtype_str == "fp16":
+        return torch.half
+    elif teacher_dtype_str == "bf16":
+        return torch.bfloat16
+    else:
+        return torch.float
+
+
+def build_model_for_eval(config, pretrained_weights):
+    model, _ = build_model_from_cfg(config, only_teacher=True)
+    dinov2_utils.load_pretrained_weights(model, pretrained_weights, "teacher")
+    model.eval()
+    model.cuda()
+    return model
+
+
+def setup_and_build_model(args) -> Tuple[Any, torch.dtype]:
+    cudnn.benchmark = True
+    config = setup(args)
+    model = build_model_for_eval(config, args.pretrained_weights)
+    autocast_dtype = get_autocast_dtype(config)
+    return model, autocast_dtype
diff --git a/src/dinov2/eval/utils.py b/src/dinov2/eval/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c50576b1940587ee64b7a422e2e96b475d60fd39
--- /dev/null
+++ b/src/dinov2/eval/utils.py
@@ -0,0 +1,146 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+from typing import Dict, Optional
+
+import torch
+from torch import nn
+from torchmetrics import MetricCollection
+
+from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader
+import dinov2.distributed as distributed
+from dinov2.logging import MetricLogger
+
+
+logger = logging.getLogger("dinov2")
+
+
+class ModelWithNormalize(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, samples):
+        return nn.functional.normalize(self.model(samples), dim=1, p=2)
+
+
+class ModelWithIntermediateLayers(nn.Module):
+    def __init__(self, feature_model, n_last_blocks, autocast_ctx):
+        super().__init__()
+        self.feature_model = feature_model
+        self.feature_model.eval()
+        self.n_last_blocks = n_last_blocks
+        self.autocast_ctx = autocast_ctx
+
+    def forward(self, images):
+        with torch.inference_mode():
+            with self.autocast_ctx():
+                features = self.feature_model.get_intermediate_layers(
+                    images, self.n_last_blocks, return_class_token=True
+                )
+        return features
+
+
+@torch.inference_mode()
+def evaluate(
+    model: nn.Module,
+    data_loader,
+    postprocessors: Dict[str, nn.Module],
+    metrics: Dict[str, MetricCollection],
+    device: torch.device,
+    criterion: Optional[nn.Module] = None,
+):
+    model.eval()
+    if criterion is not None:
+        criterion.eval()
+
+    for metric in metrics.values():
+        metric = metric.to(device)
+
+    metric_logger = MetricLogger(delimiter="  ")
+    header = "Test:"
+
+    for samples, targets, *_ in metric_logger.log_every(data_loader, 10, header):
+        outputs = model(samples.to(device))
+        targets = targets.to(device)
+
+        if criterion is not None:
+            loss = criterion(outputs, targets)
+            metric_logger.update(loss=loss.item())
+
+        for k, metric in metrics.items():
+            metric_inputs = postprocessors[k](outputs, targets)
+            metric.update(**metric_inputs)
+
+    metric_logger.synchronize_between_processes()
+    logger.info(f"Averaged stats: {metric_logger}")
+
+    stats = {k: metric.compute() for k, metric in metrics.items()}
+    metric_logger_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
+    return metric_logger_stats, stats
+
+
+def all_gather_and_flatten(tensor_rank):
+    tensor_all_ranks = torch.empty(
+        distributed.get_global_size(),
+        *tensor_rank.shape,
+        dtype=tensor_rank.dtype,
+        device=tensor_rank.device,
+    )
+    tensor_list = list(tensor_all_ranks.unbind(0))
+    torch.distributed.all_gather(tensor_list, tensor_rank.contiguous())
+    return tensor_all_ranks.flatten(end_dim=1)
+
+
+def extract_features(model, dataset, batch_size, num_workers, gather_on_cpu=False):
+    dataset_with_enumerated_targets = DatasetWithEnumeratedTargets(dataset)
+    sample_count = len(dataset_with_enumerated_targets)
+    data_loader = make_data_loader(
+        dataset=dataset_with_enumerated_targets,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        sampler_type=SamplerType.DISTRIBUTED,
+        drop_last=False,
+        shuffle=False,
+    )
+    return extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu)
+
+
+@torch.inference_mode()
+def extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu=False):
+    gather_device = torch.device("cpu") if gather_on_cpu else torch.device("cuda")
+    metric_logger = MetricLogger(delimiter="  ")
+    features, all_labels = None, None
+    for samples, (index, labels_rank) in metric_logger.log_every(data_loader, 10):
+        samples = samples.cuda(non_blocking=True)
+        labels_rank = labels_rank.cuda(non_blocking=True)
+        index = index.cuda(non_blocking=True)
+        features_rank = model(samples).float()
+
+        # init storage feature matrix
+        if features is None:
+            features = torch.zeros(sample_count, features_rank.shape[-1], device=gather_device)
+            labels_shape = list(labels_rank.shape)
+            labels_shape[0] = sample_count
+            all_labels = torch.full(labels_shape, fill_value=-1, device=gather_device)
+            logger.info(f"Storing features into tensor of shape {features.shape}")
+
+        # share indexes, features and labels between processes
+        index_all = all_gather_and_flatten(index).to(gather_device)
+        features_all_ranks = all_gather_and_flatten(features_rank).to(gather_device)
+        labels_all_ranks = all_gather_and_flatten(labels_rank).to(gather_device)
+
+        # update storage feature matrix
+        if len(index_all) > 0:
+            features.index_copy_(0, index_all, features_all_ranks)
+            all_labels.index_copy_(0, index_all, labels_all_ranks)
+
+    logger.info(f"Features shape: {tuple(features.shape)}")
+    logger.info(f"Labels shape: {tuple(all_labels.shape)}")
+
+    assert torch.all(all_labels > -1)
+
+    return features, all_labels
diff --git a/src/dinov2/fsdp/__init__.py b/src/dinov2/fsdp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed454480e0b76e761d657cc40fd097bd339d15a2
--- /dev/null
+++ b/src/dinov2/fsdp/__init__.py
@@ -0,0 +1,157 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import os
+from typing import Any
+
+import torch
+import dinov2.distributed as distributed
+from functools import partial
+from fvcore.common.checkpoint import Checkpointer
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp import ShardingStrategy
+from torch.distributed.fsdp import MixedPrecision
+from torch.distributed.fsdp import StateDictType
+from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
+from torch.distributed.fsdp.wrap import ModuleWrapPolicy
+from torch.distributed.fsdp._runtime_utils import _reshard
+
+
+def get_fsdp_wrapper(model_cfg, modules_to_wrap=set()):
+    sharding_strategy_dict = {
+        "NO_SHARD": ShardingStrategy.NO_SHARD,
+        "SHARD_GRAD_OP": ShardingStrategy.SHARD_GRAD_OP,
+        "FULL_SHARD": ShardingStrategy.FULL_SHARD,
+    }
+
+    dtype_dict = {
+        "fp32": torch.float32,
+        "fp16": torch.float16,
+        "bf16": torch.bfloat16,
+    }
+
+    mixed_precision_config = MixedPrecision(
+        param_dtype=dtype_dict[model_cfg.mixed_precision.param_dtype],
+        reduce_dtype=dtype_dict[model_cfg.mixed_precision.reduce_dtype],
+        buffer_dtype=dtype_dict[model_cfg.mixed_precision.buffer_dtype],
+    )
+
+    sharding_strategy_config = sharding_strategy_dict[model_cfg.sharding_strategy]
+
+    local_rank = distributed.get_local_rank()
+
+    fsdp_wrapper = partial(
+        FSDP,
+        sharding_strategy=sharding_strategy_config,
+        mixed_precision=mixed_precision_config,
+        device_id=local_rank,
+        sync_module_states=True,
+        use_orig_params=True,
+        auto_wrap_policy=ModuleWrapPolicy(modules_to_wrap),
+    )
+    return fsdp_wrapper
+
+
+def is_fsdp(x):
+    return isinstance(x, FSDP)
+
+
+def is_sharded_fsdp(x):
+    return is_fsdp(x) and x.sharding_strategy is not ShardingStrategy.NO_SHARD
+
+
+def free_if_fsdp(x):
+    if is_sharded_fsdp(x):
+        handles = x._handles
+        true_list = [True for h in handles]
+        _reshard(x, handles, true_list)
+
+
+def get_fsdp_modules(x):
+    return FSDP.fsdp_modules(x)
+
+
+def reshard_fsdp_model(x):
+    for m in get_fsdp_modules(x):
+        free_if_fsdp(m)
+
+
+def rankstr():
+    return f"rank_{distributed.get_global_rank()}"
+
+
+class FSDPCheckpointer(Checkpointer):
+    def save(self, name: str, **kwargs: Any) -> None:
+        """
+        Dump model and checkpointables to a file.
+
+        Args:
+            name (str): name of the file.
+            kwargs (dict): extra arbitrary data to save.
+        """
+        if not self.save_dir or not self.save_to_disk:
+            return
+
+        data = {}
+        with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT):
+            data["model"] = self.model.state_dict()
+
+        # data["model"] = self.model.state_dict()
+        for key, obj in self.checkpointables.items():
+            data[key] = obj.state_dict()
+        data.update(kwargs)
+
+        basename = f"{name}.{rankstr()}.pth"
+        save_file = os.path.join(self.save_dir, basename)
+        assert os.path.basename(save_file) == basename, basename
+        self.logger.info("Saving checkpoint to {}".format(save_file))
+        with self.path_manager.open(save_file, "wb") as f:
+            torch.save(data, f)
+        self.tag_last_checkpoint(basename)
+
+    def load(self, *args, **kwargs):
+        with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT):
+            return super().load(*args, **kwargs)
+
+    def has_checkpoint(self) -> bool:
+        """
+        Returns:
+            bool: whether a checkpoint exists in the target directory.
+        """
+        save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
+        return self.path_manager.exists(save_file)
+
+    def get_checkpoint_file(self) -> str:
+        """
+        Returns:
+            str: The latest checkpoint file in target directory.
+        """
+        save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
+        try:
+            with self.path_manager.open(save_file, "r") as f:
+                last_saved = f.read().strip()
+        except IOError:
+            # if file doesn't exist, maybe because it has just been
+            # deleted by a separate process
+            return ""
+        # pyre-fixme[6]: For 2nd param expected `Union[PathLike[str], str]` but got
+        #  `Union[bytes, str]`.
+        return os.path.join(self.save_dir, last_saved)
+
+    def tag_last_checkpoint(self, last_filename_basename: str) -> None:
+        """
+        Tag the last checkpoint.
+
+        Args:
+            last_filename_basename (str): the basename of the last filename.
+        """
+        if distributed.is_enabled():
+            torch.distributed.barrier()
+        save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
+        with self.path_manager.open(save_file, "w") as f:
+            f.write(last_filename_basename)  # pyre-ignore
+
+
+ShardedGradScaler = ShardedGradScaler
diff --git a/src/dinov2/hub/__init__.py b/src/dinov2/hub/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9
--- /dev/null
+++ b/src/dinov2/hub/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/src/dinov2/hub/backbones.py b/src/dinov2/hub/backbones.py
new file mode 100644
index 0000000000000000000000000000000000000000..53fe83719d5107eb77a8f25ef1814c3d73446002
--- /dev/null
+++ b/src/dinov2/hub/backbones.py
@@ -0,0 +1,156 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import Union
+
+import torch
+
+from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name
+
+
+class Weights(Enum):
+    LVD142M = "LVD142M"
+
+
+def _make_dinov2_model(
+    *,
+    arch_name: str = "vit_large",
+    img_size: int = 518,
+    patch_size: int = 14,
+    init_values: float = 1.0,
+    ffn_layer: str = "mlp",
+    block_chunks: int = 0,
+    num_register_tokens: int = 0,
+    interpolate_antialias: bool = False,
+    interpolate_offset: float = 0.1,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.LVD142M,
+    **kwargs,
+):
+    from ..models import vision_transformer as vits
+
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    model_base_name = _make_dinov2_model_name(arch_name, patch_size)
+    vit_kwargs = dict(
+        img_size=img_size,
+        patch_size=patch_size,
+        init_values=init_values,
+        ffn_layer=ffn_layer,
+        block_chunks=block_chunks,
+        num_register_tokens=num_register_tokens,
+        interpolate_antialias=interpolate_antialias,
+        interpolate_offset=interpolate_offset,
+    )
+    vit_kwargs.update(**kwargs)
+    model = vits.__dict__[arch_name](**vit_kwargs)
+
+    if pretrained:
+        model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens)
+        url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_pretrain.pth"
+        state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        model.load_state_dict(state_dict, strict=True)
+
+    return model
+
+
+def dinov2_vits14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitb14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-B/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitl14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitg14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-g/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_giant2",
+        ffn_layer="swiglufused",
+        weights=weights,
+        pretrained=pretrained,
+        **kwargs,
+    )
+
+
+def dinov2_vits14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-S/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_small",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-B/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_base",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-L/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_large",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-g/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_giant2",
+        ffn_layer="swiglufused",
+        weights=weights,
+        pretrained=pretrained,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
diff --git a/src/dinov2/hub/classifiers.py b/src/dinov2/hub/classifiers.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f0841efa80ab3d564cd320d61da254af182606b
--- /dev/null
+++ b/src/dinov2/hub/classifiers.py
@@ -0,0 +1,268 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import Union
+
+import torch
+import torch.nn as nn
+
+from .backbones import _make_dinov2_model
+from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name
+
+
+class Weights(Enum):
+    IMAGENET1K = "IMAGENET1K"
+
+
+def _make_dinov2_linear_classification_head(
+    *,
+    arch_name: str = "vit_large",
+    patch_size: int = 14,
+    embed_dim: int = 1024,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    num_register_tokens: int = 0,
+    **kwargs,
+):
+    if layers not in (1, 4):
+        raise AssertionError(f"Unsupported number of layers: {layers}")
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    linear_head = nn.Linear((1 + layers) * embed_dim, 1_000)
+
+    if pretrained:
+        model_base_name = _make_dinov2_model_name(arch_name, patch_size)
+        model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens)
+        layers_str = str(layers) if layers == 4 else ""
+        url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_linear{layers_str}_head.pth"
+        state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        linear_head.load_state_dict(state_dict, strict=True)
+
+    return linear_head
+
+
+class _LinearClassifierWrapper(nn.Module):
+    def __init__(self, *, backbone: nn.Module, linear_head: nn.Module, layers: int = 4):
+        super().__init__()
+        self.backbone = backbone
+        self.linear_head = linear_head
+        self.layers = layers
+
+    def forward(self, x):
+        if self.layers == 1:
+            x = self.backbone.forward_features(x)
+            cls_token = x["x_norm_clstoken"]
+            patch_tokens = x["x_norm_patchtokens"]
+            # fmt: off
+            linear_input = torch.cat([
+                cls_token,
+                patch_tokens.mean(dim=1),
+            ], dim=1)
+            # fmt: on
+        elif self.layers == 4:
+            x = self.backbone.get_intermediate_layers(x, n=4, return_class_token=True)
+            # fmt: off
+            linear_input = torch.cat([
+                x[0][1],
+                x[1][1],
+                x[2][1],
+                x[3][1],
+                x[3][0].mean(dim=1),
+            ], dim=1)
+            # fmt: on
+        else:
+            assert False, f"Unsupported number of layers: {self.layers}"
+        return self.linear_head(linear_input)
+
+
+def _make_dinov2_linear_classifier(
+    *,
+    arch_name: str = "vit_large",
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    num_register_tokens: int = 0,
+    interpolate_antialias: bool = False,
+    interpolate_offset: float = 0.1,
+    **kwargs,
+):
+    backbone = _make_dinov2_model(
+        arch_name=arch_name,
+        pretrained=pretrained,
+        num_register_tokens=num_register_tokens,
+        interpolate_antialias=interpolate_antialias,
+        interpolate_offset=interpolate_offset,
+        **kwargs,
+    )
+
+    embed_dim = backbone.embed_dim
+    patch_size = backbone.patch_size
+    linear_head = _make_dinov2_linear_classification_head(
+        arch_name=arch_name,
+        patch_size=patch_size,
+        embed_dim=embed_dim,
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=num_register_tokens,
+    )
+
+    return _LinearClassifierWrapper(backbone=backbone, linear_head=linear_head, layers=layers)
+
+
+def dinov2_vits14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_small",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_base",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_large",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_giant2",
+        layers=layers,
+        ffn_layer="swiglufused",
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vits14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_small",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_base",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_large",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_giant2",
+        layers=layers,
+        ffn_layer="swiglufused",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
diff --git a/src/dinov2/hub/depth/__init__.py b/src/dinov2/hub/depth/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..91716e58ab6158d814df8c653644d9af4c7be65c
--- /dev/null
+++ b/src/dinov2/hub/depth/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .decode_heads import BNHead, DPTHead
+from .encoder_decoder import DepthEncoderDecoder
diff --git a/src/dinov2/hub/depth/decode_heads.py b/src/dinov2/hub/depth/decode_heads.py
new file mode 100644
index 0000000000000000000000000000000000000000..f455accad38fec6ecdd53460233a564c34f434da
--- /dev/null
+++ b/src/dinov2/hub/depth/decode_heads.py
@@ -0,0 +1,747 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import copy
+from functools import partial
+import math
+import warnings
+
+import torch
+import torch.nn as nn
+
+from .ops import resize
+
+
+# XXX: (Untested) replacement for mmcv.imdenormalize()
+def _imdenormalize(img, mean, std, to_bgr=True):
+    import numpy as np
+
+    mean = mean.reshape(1, -1).astype(np.float64)
+    std = std.reshape(1, -1).astype(np.float64)
+    img = (img * std) + mean
+    if to_bgr:
+        img = img[::-1]
+    return img
+
+
+class DepthBaseDecodeHead(nn.Module):
+    """Base class for BaseDecodeHead.
+
+    Args:
+        in_channels (List): Input channels.
+        channels (int): Channels after modules, before conv_depth.
+        conv_layer (nn.Module): Conv layers. Default: None.
+        act_layer (nn.Module): Activation layers. Default: nn.ReLU.
+        loss_decode (dict): Config of decode loss.
+            Default: ().
+        sampler (dict|None): The config of depth map sampler.
+            Default: None.
+        align_corners (bool): align_corners argument of F.interpolate.
+            Default: False.
+        min_depth (int): Min depth in dataset setting.
+            Default: 1e-3.
+        max_depth (int): Max depth in dataset setting.
+            Default: None.
+        norm_layer (dict|None): Norm layers.
+            Default: None.
+        classify (bool): Whether predict depth in a cls.-reg. manner.
+            Default: False.
+        n_bins (int): The number of bins used in cls. step.
+            Default: 256.
+        bins_strategy (str): The discrete strategy used in cls. step.
+            Default: 'UD'.
+        norm_strategy (str): The norm strategy on cls. probability
+            distribution. Default: 'linear'
+        scale_up (str): Whether predict depth in a scale-up manner.
+            Default: False.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        conv_layer=None,
+        act_layer=nn.ReLU,
+        channels=96,
+        loss_decode=(),
+        sampler=None,
+        align_corners=False,
+        min_depth=1e-3,
+        max_depth=None,
+        norm_layer=None,
+        classify=False,
+        n_bins=256,
+        bins_strategy="UD",
+        norm_strategy="linear",
+        scale_up=False,
+    ):
+        super(DepthBaseDecodeHead, self).__init__()
+
+        self.in_channels = in_channels
+        self.channels = channels
+        self.conf_layer = conv_layer
+        self.act_layer = act_layer
+        self.loss_decode = loss_decode
+        self.align_corners = align_corners
+        self.min_depth = min_depth
+        self.max_depth = max_depth
+        self.norm_layer = norm_layer
+        self.classify = classify
+        self.n_bins = n_bins
+        self.scale_up = scale_up
+
+        if self.classify:
+            assert bins_strategy in ["UD", "SID"], "Support bins_strategy: UD, SID"
+            assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid"
+
+            self.bins_strategy = bins_strategy
+            self.norm_strategy = norm_strategy
+            self.softmax = nn.Softmax(dim=1)
+            self.conv_depth = nn.Conv2d(channels, n_bins, kernel_size=3, padding=1, stride=1)
+        else:
+            self.conv_depth = nn.Conv2d(channels, 1, kernel_size=3, padding=1, stride=1)
+
+        self.relu = nn.ReLU()
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, inputs, img_metas):
+        """Placeholder of forward function."""
+        pass
+
+    def forward_train(self, img, inputs, img_metas, depth_gt):
+        """Forward function for training.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            depth_gt (Tensor): GT depth
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        depth_pred = self.forward(inputs, img_metas)
+        losses = self.losses(depth_pred, depth_gt)
+
+        log_imgs = self.log_images(img[0], depth_pred[0], depth_gt[0], img_metas[0])
+        losses.update(**log_imgs)
+
+        return losses
+
+    def forward_test(self, inputs, img_metas):
+        """Forward function for testing.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+
+        Returns:
+            Tensor: Output depth map.
+        """
+        return self.forward(inputs, img_metas)
+
+    def depth_pred(self, feat):
+        """Prediction each pixel."""
+        if self.classify:
+            logit = self.conv_depth(feat)
+
+            if self.bins_strategy == "UD":
+                bins = torch.linspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
+            elif self.bins_strategy == "SID":
+                bins = torch.logspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
+
+            # following Adabins, default linear
+            if self.norm_strategy == "linear":
+                logit = torch.relu(logit)
+                eps = 0.1
+                logit = logit + eps
+                logit = logit / logit.sum(dim=1, keepdim=True)
+            elif self.norm_strategy == "softmax":
+                logit = torch.softmax(logit, dim=1)
+            elif self.norm_strategy == "sigmoid":
+                logit = torch.sigmoid(logit)
+                logit = logit / logit.sum(dim=1, keepdim=True)
+
+            output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1)
+
+        else:
+            if self.scale_up:
+                output = self.sigmoid(self.conv_depth(feat)) * self.max_depth
+            else:
+                output = self.relu(self.conv_depth(feat)) + self.min_depth
+        return output
+
+    def losses(self, depth_pred, depth_gt):
+        """Compute depth loss."""
+        loss = dict()
+        depth_pred = resize(
+            input=depth_pred, size=depth_gt.shape[2:], mode="bilinear", align_corners=self.align_corners, warning=False
+        )
+        if not isinstance(self.loss_decode, nn.ModuleList):
+            losses_decode = [self.loss_decode]
+        else:
+            losses_decode = self.loss_decode
+        for loss_decode in losses_decode:
+            if loss_decode.loss_name not in loss:
+                loss[loss_decode.loss_name] = loss_decode(depth_pred, depth_gt)
+            else:
+                loss[loss_decode.loss_name] += loss_decode(depth_pred, depth_gt)
+        return loss
+
+    def log_images(self, img_path, depth_pred, depth_gt, img_meta):
+        import numpy as np
+
+        show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0))
+        show_img = show_img.numpy().astype(np.float32)
+        show_img = _imdenormalize(
+            show_img,
+            img_meta["img_norm_cfg"]["mean"],
+            img_meta["img_norm_cfg"]["std"],
+            img_meta["img_norm_cfg"]["to_rgb"],
+        )
+        show_img = np.clip(show_img, 0, 255)
+        show_img = show_img.astype(np.uint8)
+        show_img = show_img[:, :, ::-1]
+        show_img = show_img.transpose(0, 2, 1)
+        show_img = show_img.transpose(1, 0, 2)
+
+        depth_pred = depth_pred / torch.max(depth_pred)
+        depth_gt = depth_gt / torch.max(depth_gt)
+
+        depth_pred_color = copy.deepcopy(depth_pred.detach().cpu())
+        depth_gt_color = copy.deepcopy(depth_gt.detach().cpu())
+
+        return {"img_rgb": show_img, "img_depth_pred": depth_pred_color, "img_depth_gt": depth_gt_color}
+
+
+class BNHead(DepthBaseDecodeHead):
+    """Just a batchnorm."""
+
+    def __init__(self, input_transform="resize_concat", in_index=(0, 1, 2, 3), upsample=1, **kwargs):
+        super().__init__(**kwargs)
+        self.input_transform = input_transform
+        self.in_index = in_index
+        self.upsample = upsample
+        # self.bn = nn.SyncBatchNorm(self.in_channels)
+        if self.classify:
+            self.conv_depth = nn.Conv2d(self.channels, self.n_bins, kernel_size=1, padding=0, stride=1)
+        else:
+            self.conv_depth = nn.Conv2d(self.channels, 1, kernel_size=1, padding=0, stride=1)
+
+    def _transform_inputs(self, inputs):
+        """Transform inputs for decoder.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            Tensor: The transformed inputs
+        """
+
+        if "concat" in self.input_transform:
+            inputs = [inputs[i] for i in self.in_index]
+            if "resize" in self.input_transform:
+                inputs = [
+                    resize(
+                        input=x,
+                        size=[s * self.upsample for s in inputs[0].shape[2:]],
+                        mode="bilinear",
+                        align_corners=self.align_corners,
+                    )
+                    for x in inputs
+                ]
+            inputs = torch.cat(inputs, dim=1)
+        elif self.input_transform == "multiple_select":
+            inputs = [inputs[i] for i in self.in_index]
+        else:
+            inputs = inputs[self.in_index]
+
+        return inputs
+
+    def _forward_feature(self, inputs, img_metas=None, **kwargs):
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
+        # accept lists (for cls token)
+        inputs = list(inputs)
+        for i, x in enumerate(inputs):
+            if len(x) == 2:
+                x, cls_token = x[0], x[1]
+                if len(x.shape) == 2:
+                    x = x[:, :, None, None]
+                cls_token = cls_token[:, :, None, None].expand_as(x)
+                inputs[i] = torch.cat((x, cls_token), 1)
+            else:
+                x = x[0]
+                if len(x.shape) == 2:
+                    x = x[:, :, None, None]
+                inputs[i] = x
+        x = self._transform_inputs(inputs)
+        # feats = self.bn(x)
+        return x
+
+    def forward(self, inputs, img_metas=None, **kwargs):
+        """Forward function."""
+        output = self._forward_feature(inputs, img_metas=img_metas, **kwargs)
+        output = self.depth_pred(output)
+        return output
+
+
+class ConvModule(nn.Module):
+    """A conv block that bundles conv/norm/activation layers.
+
+    This block simplifies the usage of convolution layers, which are commonly
+    used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
+    It is based upon three build methods: `build_conv_layer()`,
+    `build_norm_layer()` and `build_activation_layer()`.
+
+    Besides, we add some additional features in this module.
+    1. Automatically set `bias` of the conv layer.
+    2. Spectral norm is supported.
+    3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
+    supports zero and circular padding, and we add "reflect" padding mode.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+            Same as that in ``nn._ConvNd``.
+        out_channels (int): Number of channels produced by the convolution.
+            Same as that in ``nn._ConvNd``.
+        kernel_size (int | tuple[int]): Size of the convolving kernel.
+            Same as that in ``nn._ConvNd``.
+        stride (int | tuple[int]): Stride of the convolution.
+            Same as that in ``nn._ConvNd``.
+        padding (int | tuple[int]): Zero-padding added to both sides of
+            the input. Same as that in ``nn._ConvNd``.
+        dilation (int | tuple[int]): Spacing between kernel elements.
+            Same as that in ``nn._ConvNd``.
+        groups (int): Number of blocked connections from input channels to
+            output channels. Same as that in ``nn._ConvNd``.
+        bias (bool | str): If specified as `auto`, it will be decided by the
+            norm_layer. Bias will be set as True if `norm_layer` is None, otherwise
+            False. Default: "auto".
+        conv_layer (nn.Module): Convolution layer. Default: None,
+            which means using conv2d.
+        norm_layer (nn.Module): Normalization layer. Default: None.
+        act_layer (nn.Module): Activation layer. Default: nn.ReLU.
+        inplace (bool): Whether to use inplace mode for activation.
+            Default: True.
+        with_spectral_norm (bool): Whether use spectral norm in conv module.
+            Default: False.
+        padding_mode (str): If the `padding_mode` has not been supported by
+            current `Conv2d` in PyTorch, we will use our own padding layer
+            instead. Currently, we support ['zeros', 'circular'] with official
+            implementation and ['reflect'] with our own implementation.
+            Default: 'zeros'.
+        order (tuple[str]): The order of conv/norm/activation layers. It is a
+            sequence of "conv", "norm" and "act". Common examples are
+            ("conv", "norm", "act") and ("act", "conv", "norm").
+            Default: ('conv', 'norm', 'act').
+    """
+
+    _abbr_ = "conv_block"
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias="auto",
+        conv_layer=nn.Conv2d,
+        norm_layer=None,
+        act_layer=nn.ReLU,
+        inplace=True,
+        with_spectral_norm=False,
+        padding_mode="zeros",
+        order=("conv", "norm", "act"),
+    ):
+        super(ConvModule, self).__init__()
+        official_padding_mode = ["zeros", "circular"]
+        self.conv_layer = conv_layer
+        self.norm_layer = norm_layer
+        self.act_layer = act_layer
+        self.inplace = inplace
+        self.with_spectral_norm = with_spectral_norm
+        self.with_explicit_padding = padding_mode not in official_padding_mode
+        self.order = order
+        assert isinstance(self.order, tuple) and len(self.order) == 3
+        assert set(order) == set(["conv", "norm", "act"])
+
+        self.with_norm = norm_layer is not None
+        self.with_activation = act_layer is not None
+        # if the conv layer is before a norm layer, bias is unnecessary.
+        if bias == "auto":
+            bias = not self.with_norm
+        self.with_bias = bias
+
+        if self.with_explicit_padding:
+            if padding_mode == "zeros":
+                padding_layer = nn.ZeroPad2d
+            else:
+                raise AssertionError(f"Unsupported padding mode: {padding_mode}")
+            self.pad = padding_layer(padding)
+
+        # reset padding to 0 for conv module
+        conv_padding = 0 if self.with_explicit_padding else padding
+        # build convolution layer
+        self.conv = self.conv_layer(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=conv_padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+        # export the attributes of self.conv to a higher level for convenience
+        self.in_channels = self.conv.in_channels
+        self.out_channels = self.conv.out_channels
+        self.kernel_size = self.conv.kernel_size
+        self.stride = self.conv.stride
+        self.padding = padding
+        self.dilation = self.conv.dilation
+        self.transposed = self.conv.transposed
+        self.output_padding = self.conv.output_padding
+        self.groups = self.conv.groups
+
+        if self.with_spectral_norm:
+            self.conv = nn.utils.spectral_norm(self.conv)
+
+        # build normalization layers
+        if self.with_norm:
+            # norm layer is after conv layer
+            if order.index("norm") > order.index("conv"):
+                norm_channels = out_channels
+            else:
+                norm_channels = in_channels
+            norm = partial(norm_layer, num_features=norm_channels)
+            self.add_module("norm", norm)
+            if self.with_bias:
+                from torch.nnModules.batchnorm import _BatchNorm
+                from torch.nnModules.instancenorm import _InstanceNorm
+
+                if isinstance(norm, (_BatchNorm, _InstanceNorm)):
+                    warnings.warn("Unnecessary conv bias before batch/instance norm")
+        else:
+            self.norm_name = None
+
+        # build activation layer
+        if self.with_activation:
+            # nn.Tanh has no 'inplace' argument
+            # (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.HSigmoid, nn.Swish, nn.GELU)
+            if not isinstance(act_layer, (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.GELU)):
+                act_layer = partial(act_layer, inplace=inplace)
+            self.activate = act_layer()
+
+        # Use msra init by default
+        self.init_weights()
+
+    @property
+    def norm(self):
+        if self.norm_name:
+            return getattr(self, self.norm_name)
+        else:
+            return None
+
+    def init_weights(self):
+        # 1. It is mainly for customized conv layers with their own
+        #    initialization manners by calling their own ``init_weights()``,
+        #    and we do not want ConvModule to override the initialization.
+        # 2. For customized conv layers without their own initialization
+        #    manners (that is, they don't have their own ``init_weights()``)
+        #    and PyTorch's conv layers, they will be initialized by
+        #    this method with default ``kaiming_init``.
+        # Note: For PyTorch's conv layers, they will be overwritten by our
+        #    initialization implementation using default ``kaiming_init``.
+        if not hasattr(self.conv, "init_weights"):
+            if self.with_activation and isinstance(self.act_layer, nn.LeakyReLU):
+                nonlinearity = "leaky_relu"
+                a = 0.01  # XXX: default negative_slope
+            else:
+                nonlinearity = "relu"
+                a = 0
+            if hasattr(self.conv, "weight") and self.conv.weight is not None:
+                nn.init.kaiming_normal_(self.conv.weight, a=a, mode="fan_out", nonlinearity=nonlinearity)
+            if hasattr(self.conv, "bias") and self.conv.bias is not None:
+                nn.init.constant_(self.conv.bias, 0)
+        if self.with_norm:
+            if hasattr(self.norm, "weight") and self.norm.weight is not None:
+                nn.init.constant_(self.norm.weight, 1)
+            if hasattr(self.norm, "bias") and self.norm.bias is not None:
+                nn.init.constant_(self.norm.bias, 0)
+
+    def forward(self, x, activate=True, norm=True):
+        for layer in self.order:
+            if layer == "conv":
+                if self.with_explicit_padding:
+                    x = self.pad(x)
+                x = self.conv(x)
+            elif layer == "norm" and norm and self.with_norm:
+                x = self.norm(x)
+            elif layer == "act" and activate and self.with_activation:
+                x = self.activate(x)
+        return x
+
+
+class Interpolate(nn.Module):
+    def __init__(self, scale_factor, mode, align_corners=False):
+        super(Interpolate, self).__init__()
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
+        return x
+
+
+class HeadDepth(nn.Module):
+    def __init__(self, features):
+        super(HeadDepth, self).__init__()
+        self.head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+        )
+
+    def forward(self, x):
+        x = self.head(x)
+        return x
+
+
+class ReassembleBlocks(nn.Module):
+    """ViTPostProcessBlock, process cls_token in ViT backbone output and
+    rearrange the feature vector to feature map.
+    Args:
+        in_channels (int): ViT feature channels. Default: 768.
+        out_channels (List): output channels of each stage.
+            Default: [96, 192, 384, 768].
+        readout_type (str): Type of readout operation. Default: 'ignore'.
+        patch_size (int): The patch size. Default: 16.
+    """
+
+    def __init__(self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16):
+        super(ReassembleBlocks, self).__init__()
+
+        assert readout_type in ["ignore", "add", "project"]
+        self.readout_type = readout_type
+        self.patch_size = patch_size
+
+        self.projects = nn.ModuleList(
+            [
+                ConvModule(
+                    in_channels=in_channels,
+                    out_channels=out_channel,
+                    kernel_size=1,
+                    act_layer=None,
+                )
+                for out_channel in out_channels
+            ]
+        )
+
+        self.resize_layers = nn.ModuleList(
+            [
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0
+                ),
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0
+                ),
+                nn.Identity(),
+                nn.Conv2d(
+                    in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1
+                ),
+            ]
+        )
+        if self.readout_type == "project":
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU()))
+
+    def forward(self, inputs):
+        assert isinstance(inputs, list)
+        out = []
+        for i, x in enumerate(inputs):
+            assert len(x) == 2
+            x, cls_token = x[0], x[1]
+            feature_shape = x.shape
+            if self.readout_type == "project":
+                x = x.flatten(2).permute((0, 2, 1))
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+                x = x.permute(0, 2, 1).reshape(feature_shape)
+            elif self.readout_type == "add":
+                x = x.flatten(2) + cls_token.unsqueeze(-1)
+                x = x.reshape(feature_shape)
+            else:
+                pass
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+            out.append(x)
+        return out
+
+
+class PreActResidualConvUnit(nn.Module):
+    """ResidualConvUnit, pre-activate residual unit.
+    Args:
+        in_channels (int): number of channels in the input feature map.
+        act_layer (nn.Module): activation layer.
+        norm_layer (nn.Module): norm layer.
+        stride (int): stride of the first block. Default: 1
+        dilation (int): dilation rate for convs layers. Default: 1.
+    """
+
+    def __init__(self, in_channels, act_layer, norm_layer, stride=1, dilation=1):
+        super(PreActResidualConvUnit, self).__init__()
+
+        self.conv1 = ConvModule(
+            in_channels,
+            in_channels,
+            3,
+            stride=stride,
+            padding=dilation,
+            dilation=dilation,
+            norm_layer=norm_layer,
+            act_layer=act_layer,
+            bias=False,
+            order=("act", "conv", "norm"),
+        )
+
+        self.conv2 = ConvModule(
+            in_channels,
+            in_channels,
+            3,
+            padding=1,
+            norm_layer=norm_layer,
+            act_layer=act_layer,
+            bias=False,
+            order=("act", "conv", "norm"),
+        )
+
+    def forward(self, inputs):
+        inputs_ = inputs.clone()
+        x = self.conv1(inputs)
+        x = self.conv2(x)
+        return x + inputs_
+
+
+class FeatureFusionBlock(nn.Module):
+    """FeatureFusionBlock, merge feature map from different stages.
+    Args:
+        in_channels (int): Input channels.
+        act_layer (nn.Module): activation layer for ResidualConvUnit.
+        norm_layer (nn.Module): normalization layer.
+        expand (bool): Whether expand the channels in post process block.
+            Default: False.
+        align_corners (bool): align_corner setting for bilinear upsample.
+            Default: True.
+    """
+
+    def __init__(self, in_channels, act_layer, norm_layer, expand=False, align_corners=True):
+        super(FeatureFusionBlock, self).__init__()
+
+        self.in_channels = in_channels
+        self.expand = expand
+        self.align_corners = align_corners
+
+        self.out_channels = in_channels
+        if self.expand:
+            self.out_channels = in_channels // 2
+
+        self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_layer=None, bias=True)
+
+        self.res_conv_unit1 = PreActResidualConvUnit(
+            in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer
+        )
+        self.res_conv_unit2 = PreActResidualConvUnit(
+            in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer
+        )
+
+    def forward(self, *inputs):
+        x = inputs[0]
+        if len(inputs) == 2:
+            if x.shape != inputs[1].shape:
+                res = resize(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False)
+            else:
+                res = inputs[1]
+            x = x + self.res_conv_unit1(res)
+        x = self.res_conv_unit2(x)
+        x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners)
+        x = self.project(x)
+        return x
+
+
+class DPTHead(DepthBaseDecodeHead):
+    """Vision Transformers for Dense Prediction.
+    This head is implemented of `DPT <https://arxiv.org/abs/2103.13413>`_.
+    Args:
+        embed_dims (int): The embed dimension of the ViT backbone.
+            Default: 768.
+        post_process_channels (List): Out channels of post process conv
+            layers. Default: [96, 192, 384, 768].
+        readout_type (str): Type of readout operation. Default: 'ignore'.
+        patch_size (int): The patch size. Default: 16.
+        expand_channels (bool): Whether expand the channels in post process
+            block. Default: False.
+    """
+
+    def __init__(
+        self,
+        embed_dims=768,
+        post_process_channels=[96, 192, 384, 768],
+        readout_type="ignore",
+        patch_size=16,
+        expand_channels=False,
+        **kwargs,
+    ):
+        super(DPTHead, self).__init__(**kwargs)
+
+        self.in_channels = self.in_channels
+        self.expand_channels = expand_channels
+        self.reassemble_blocks = ReassembleBlocks(embed_dims, post_process_channels, readout_type, patch_size)
+
+        self.post_process_channels = [
+            channel * math.pow(2, i) if expand_channels else channel for i, channel in enumerate(post_process_channels)
+        ]
+        self.convs = nn.ModuleList()
+        for channel in self.post_process_channels:
+            self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_layer=None, bias=False))
+        self.fusion_blocks = nn.ModuleList()
+        for _ in range(len(self.convs)):
+            self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_layer, self.norm_layer))
+        self.fusion_blocks[0].res_conv_unit1 = None
+        self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_layer=self.norm_layer)
+        self.num_fusion_blocks = len(self.fusion_blocks)
+        self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers)
+        self.num_post_process_channels = len(self.post_process_channels)
+        assert self.num_fusion_blocks == self.num_reassemble_blocks
+        assert self.num_reassemble_blocks == self.num_post_process_channels
+        self.conv_depth = HeadDepth(self.channels)
+
+    def forward(self, inputs, img_metas):
+        assert len(inputs) == self.num_reassemble_blocks
+        x = [inp for inp in inputs]
+        x = self.reassemble_blocks(x)
+        x = [self.convs[i](feature) for i, feature in enumerate(x)]
+        out = self.fusion_blocks[0](x[-1])
+        for i in range(1, len(self.fusion_blocks)):
+            out = self.fusion_blocks[i](out, x[-(i + 1)])
+        out = self.project(out)
+        out = self.depth_pred(out)
+        return out
diff --git a/src/dinov2/hub/depth/encoder_decoder.py b/src/dinov2/hub/depth/encoder_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb29ced67957a336e763b0e7c90c0eeaea36fea8
--- /dev/null
+++ b/src/dinov2/hub/depth/encoder_decoder.py
@@ -0,0 +1,351 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .ops import resize
+
+
+def add_prefix(inputs, prefix):
+    """Add prefix for dict.
+
+    Args:
+        inputs (dict): The input dict with str keys.
+        prefix (str): The prefix to add.
+
+    Returns:
+
+        dict: The dict with keys updated with ``prefix``.
+    """
+
+    outputs = dict()
+    for name, value in inputs.items():
+        outputs[f"{prefix}.{name}"] = value
+
+    return outputs
+
+
+class DepthEncoderDecoder(nn.Module):
+    """Encoder Decoder depther.
+
+    EncoderDecoder typically consists of backbone and decode_head.
+    """
+
+    def __init__(self, backbone, decode_head):
+        super(DepthEncoderDecoder, self).__init__()
+
+        self.backbone = backbone
+        self.decode_head = decode_head
+        self.align_corners = self.decode_head.align_corners
+
+    def extract_feat(self, img):
+        """Extract features from images."""
+        return self.backbone(img)
+
+    def encode_decode(self, img, img_metas, rescale=True, size=None):
+        """Encode images with backbone and decode into a depth estimation
+        map of the same size as input."""
+        x = self.extract_feat(img)
+        out = self._decode_head_forward_test(x, img_metas)
+        # crop the pred depth to the certain range.
+        out = torch.clamp(out, min=self.decode_head.min_depth, max=self.decode_head.max_depth)
+        if rescale:
+            if size is None:
+                if img_metas is not None:
+                    size = img_metas[0]["ori_shape"][:2]
+                else:
+                    size = img.shape[2:]
+            out = resize(input=out, size=size, mode="bilinear", align_corners=self.align_corners)
+        return out
+
+    def _decode_head_forward_train(self, img, x, img_metas, depth_gt, **kwargs):
+        """Run forward function and calculate loss for decode head in
+        training."""
+        losses = dict()
+        loss_decode = self.decode_head.forward_train(img, x, img_metas, depth_gt, **kwargs)
+        losses.update(add_prefix(loss_decode, "decode"))
+        return losses
+
+    def _decode_head_forward_test(self, x, img_metas):
+        """Run forward function and calculate loss for decode head in
+        inference."""
+        depth_pred = self.decode_head.forward_test(x, img_metas)
+        return depth_pred
+
+    def forward_dummy(self, img):
+        """Dummy forward function."""
+        depth = self.encode_decode(img, None)
+
+        return depth
+
+    def forward_train(self, img, img_metas, depth_gt, **kwargs):
+        """Forward function for training.
+
+        Args:
+            img (Tensor): Input images.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            depth_gt (Tensor): Depth gt
+                used if the architecture supports depth estimation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+
+        x = self.extract_feat(img)
+
+        losses = dict()
+
+        # the last of x saves the info from neck
+        loss_decode = self._decode_head_forward_train(img, x, img_metas, depth_gt, **kwargs)
+
+        losses.update(loss_decode)
+
+        return losses
+
+    def whole_inference(self, img, img_meta, rescale, size=None):
+        """Inference with full image."""
+        return self.encode_decode(img, img_meta, rescale, size=size)
+
+    def slide_inference(self, img, img_meta, rescale, stride, crop_size):
+        """Inference by sliding-window with overlap.
+
+        If h_crop > h_img or w_crop > w_img, the small patch will be used to
+        decode without padding.
+        """
+
+        h_stride, w_stride = stride
+        h_crop, w_crop = crop_size
+        batch_size, _, h_img, w_img = img.size()
+        h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
+        w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
+        preds = img.new_zeros((batch_size, 1, h_img, w_img))
+        count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
+        for h_idx in range(h_grids):
+            for w_idx in range(w_grids):
+                y1 = h_idx * h_stride
+                x1 = w_idx * w_stride
+                y2 = min(y1 + h_crop, h_img)
+                x2 = min(x1 + w_crop, w_img)
+                y1 = max(y2 - h_crop, 0)
+                x1 = max(x2 - w_crop, 0)
+                crop_img = img[:, :, y1:y2, x1:x2]
+                depth_pred = self.encode_decode(crop_img, img_meta, rescale)
+                preds += F.pad(depth_pred, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2)))
+
+                count_mat[:, :, y1:y2, x1:x2] += 1
+        assert (count_mat == 0).sum() == 0
+        if torch.onnx.is_in_onnx_export():
+            # cast count_mat to constant while exporting to ONNX
+            count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device)
+        preds = preds / count_mat
+        return preds
+
+    def inference(self, img, img_meta, rescale, size=None, mode="whole"):
+        """Inference with slide/whole style.
+
+        Args:
+            img (Tensor): The input image of shape (N, 3, H, W).
+            img_meta (dict): Image info dict where each dict has: 'img_shape',
+                'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            rescale (bool): Whether rescale back to original shape.
+
+        Returns:
+            Tensor: The output depth map.
+        """
+
+        assert mode in ["slide", "whole"]
+        ori_shape = img_meta[0]["ori_shape"]
+        assert all(_["ori_shape"] == ori_shape for _ in img_meta)
+        if mode == "slide":
+            depth_pred = self.slide_inference(img, img_meta, rescale)
+        else:
+            depth_pred = self.whole_inference(img, img_meta, rescale, size=size)
+        output = depth_pred
+        flip = img_meta[0]["flip"]
+        if flip:
+            flip_direction = img_meta[0]["flip_direction"]
+            assert flip_direction in ["horizontal", "vertical"]
+            if flip_direction == "horizontal":
+                output = output.flip(dims=(3,))
+            elif flip_direction == "vertical":
+                output = output.flip(dims=(2,))
+
+        return output
+
+    def simple_test(self, img, img_meta, rescale=True):
+        """Simple test with single image."""
+        depth_pred = self.inference(img, img_meta, rescale)
+        if torch.onnx.is_in_onnx_export():
+            # our inference backend only support 4D output
+            depth_pred = depth_pred.unsqueeze(0)
+            return depth_pred
+        depth_pred = depth_pred.cpu().numpy()
+        # unravel batch dim
+        depth_pred = list(depth_pred)
+        return depth_pred
+
+    def aug_test(self, imgs, img_metas, rescale=True):
+        """Test with augmentations.
+
+        Only rescale=True is supported.
+        """
+        # aug_test rescale all imgs back to ori_shape for now
+        assert rescale
+        # to save memory, we get augmented depth logit inplace
+        depth_pred = self.inference(imgs[0], img_metas[0], rescale)
+        for i in range(1, len(imgs)):
+            cur_depth_pred = self.inference(imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:])
+            depth_pred += cur_depth_pred
+        depth_pred /= len(imgs)
+        depth_pred = depth_pred.cpu().numpy()
+        # unravel batch dim
+        depth_pred = list(depth_pred)
+        return depth_pred
+
+    def forward_test(self, imgs, img_metas, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (List[List[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch.
+        """
+        for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]:
+            if not isinstance(var, list):
+                raise TypeError(f"{name} must be a list, but got " f"{type(var)}")
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})")
+        # all images in the same aug batch all of the same ori_shape and pad
+        # shape
+        for img_meta in img_metas:
+            ori_shapes = [_["ori_shape"] for _ in img_meta]
+            assert all(shape == ori_shapes[0] for shape in ori_shapes)
+            img_shapes = [_["img_shape"] for _ in img_meta]
+            assert all(shape == img_shapes[0] for shape in img_shapes)
+            pad_shapes = [_["pad_shape"] for _ in img_meta]
+            assert all(shape == pad_shapes[0] for shape in pad_shapes)
+
+        if num_augs == 1:
+            return self.simple_test(imgs[0], img_metas[0], **kwargs)
+        else:
+            return self.aug_test(imgs, img_metas, **kwargs)
+
+    def forward(self, img, img_metas, return_loss=True, **kwargs):
+        """Calls either :func:`forward_train` or :func:`forward_test` depending
+        on whether ``return_loss`` is ``True``.
+
+        Note this setting will change the expected inputs. When
+        ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
+        and List[dict]), and when ``resturn_loss=False``, img and img_meta
+        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
+        the outer list indicating test time augmentations.
+        """
+        if return_loss:
+            return self.forward_train(img, img_metas, **kwargs)
+        else:
+            return self.forward_test(img, img_metas, **kwargs)
+
+    def train_step(self, data_batch, optimizer, **kwargs):
+        """The iteration step during training.
+
+        This method defines an iteration step during training, except for the
+        back propagation and optimizer updating, which are done in an optimizer
+        hook. Note that in some complicated cases or models, the whole process
+        including back propagation and optimizer updating is also defined in
+        this method, such as GAN.
+
+        Args:
+            data (dict): The output of dataloader.
+            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
+                runner is passed to ``train_step()``. This argument is unused
+                and reserved.
+
+        Returns:
+            dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
+                ``num_samples``.
+                ``loss`` is a tensor for back propagation, which can be a
+                weighted sum of multiple losses.
+                ``log_vars`` contains all the variables to be sent to the
+                logger.
+                ``num_samples`` indicates the batch size (when the model is
+                DDP, it means the batch size on each GPU), which is used for
+                averaging the logs.
+        """
+        losses = self(**data_batch)
+
+        # split losses and images
+        real_losses = {}
+        log_imgs = {}
+        for k, v in losses.items():
+            if "img" in k:
+                log_imgs[k] = v
+            else:
+                real_losses[k] = v
+
+        loss, log_vars = self._parse_losses(real_losses)
+
+        outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs)
+
+        return outputs
+
+    def val_step(self, data_batch, **kwargs):
+        """The iteration step during validation.
+
+        This method shares the same signature as :func:`train_step`, but used
+        during val epochs. Note that the evaluation after training epochs is
+        not implemented with this method, but an evaluation hook.
+        """
+        output = self(**data_batch, **kwargs)
+        return output
+
+    @staticmethod
+    def _parse_losses(losses):
+        import torch.distributed as dist
+
+        """Parse the raw outputs (losses) of the network.
+
+        Args:
+            losses (dict): Raw output of the network, which usually contain
+                losses and other necessary information.
+
+        Returns:
+            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
+                which may be a weighted sum of all losses, log_vars contains
+                all the variables to be sent to the logger.
+        """
+        log_vars = OrderedDict()
+        for loss_name, loss_value in losses.items():
+            if isinstance(loss_value, torch.Tensor):
+                log_vars[loss_name] = loss_value.mean()
+            elif isinstance(loss_value, list):
+                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
+            else:
+                raise TypeError(f"{loss_name} is not a tensor or list of tensors")
+
+        loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key)
+
+        log_vars["loss"] = loss
+        for loss_name, loss_value in log_vars.items():
+            # reduce loss when distributed training
+            if dist.is_available() and dist.is_initialized():
+                loss_value = loss_value.data.clone()
+                dist.all_reduce(loss_value.div_(dist.get_world_size()))
+            log_vars[loss_name] = loss_value.item()
+
+        return loss, log_vars
diff --git a/src/dinov2/hub/depth/ops.py b/src/dinov2/hub/depth/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..15880ee0cb7652d4b41c489b927bf6a156b40e5e
--- /dev/null
+++ b/src/dinov2/hub/depth/ops.py
@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import warnings
+
+import torch.nn.functional as F
+
+
+def resize(input, size=None, scale_factor=None, mode="nearest", align_corners=None, warning=False):
+    if warning:
+        if size is not None and align_corners:
+            input_h, input_w = tuple(int(x) for x in input.shape[2:])
+            output_h, output_w = tuple(int(x) for x in size)
+            if output_h > input_h or output_w > output_h:
+                if (
+                    (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1)
+                    and (output_h - 1) % (input_h - 1)
+                    and (output_w - 1) % (input_w - 1)
+                ):
+                    warnings.warn(
+                        f"When align_corners={align_corners}, "
+                        "the output would more aligned if "
+                        f"input size {(input_h, input_w)} is `x+1` and "
+                        f"out size {(output_h, output_w)} is `nx+1`"
+                    )
+    return F.interpolate(input, size, scale_factor, mode, align_corners)
diff --git a/src/dinov2/hub/depthers.py b/src/dinov2/hub/depthers.py
new file mode 100644
index 0000000000000000000000000000000000000000..f88b7e9a41056594e3b3e66107feee98bffab820
--- /dev/null
+++ b/src/dinov2/hub/depthers.py
@@ -0,0 +1,246 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from functools import partial
+from typing import Optional, Tuple, Union
+
+import torch
+
+from .backbones import _make_dinov2_model
+from .depth import BNHead, DepthEncoderDecoder, DPTHead
+from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name, CenterPadding
+
+
+class Weights(Enum):
+    NYU = "NYU"
+    KITTI = "KITTI"
+
+
+def _get_depth_range(pretrained: bool, weights: Weights = Weights.NYU) -> Tuple[float, float]:
+    if not pretrained:  # Default
+        return (0.001, 10.0)
+
+    # Pretrained, set according to the training dataset for the provided weights
+    if weights == Weights.KITTI:
+        return (0.001, 80.0)
+
+    if weights == Weights.NYU:
+        return (0.001, 10.0)
+
+    return (0.001, 10.0)
+
+
+def _make_dinov2_linear_depth_head(
+    *,
+    embed_dim: int,
+    layers: int,
+    min_depth: float,
+    max_depth: float,
+    **kwargs,
+):
+    if layers not in (1, 4):
+        raise AssertionError(f"Unsupported number of layers: {layers}")
+
+    if layers == 1:
+        in_index = [0]
+    else:
+        assert layers == 4
+        in_index = [0, 1, 2, 3]
+
+    return BNHead(
+        classify=True,
+        n_bins=256,
+        bins_strategy="UD",
+        norm_strategy="linear",
+        upsample=4,
+        in_channels=[embed_dim] * len(in_index),
+        in_index=in_index,
+        input_transform="resize_concat",
+        channels=embed_dim * len(in_index) * 2,
+        align_corners=False,
+        min_depth=0.001,
+        max_depth=80,
+        loss_decode=(),
+    )
+
+
+def _make_dinov2_linear_depther(
+    *,
+    arch_name: str = "vit_large",
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.NYU,
+    depth_range: Optional[Tuple[float, float]] = None,
+    **kwargs,
+):
+    if layers not in (1, 4):
+        raise AssertionError(f"Unsupported number of layers: {layers}")
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    if depth_range is None:
+        depth_range = _get_depth_range(pretrained, weights)
+    min_depth, max_depth = depth_range
+
+    backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
+
+    embed_dim = backbone.embed_dim
+    patch_size = backbone.patch_size
+    model_name = _make_dinov2_model_name(arch_name, patch_size)
+    linear_depth_head = _make_dinov2_linear_depth_head(
+        embed_dim=embed_dim,
+        layers=layers,
+        min_depth=min_depth,
+        max_depth=max_depth,
+    )
+
+    layer_count = {
+        "vit_small": 12,
+        "vit_base": 12,
+        "vit_large": 24,
+        "vit_giant2": 40,
+    }[arch_name]
+
+    if layers == 4:
+        out_index = {
+            "vit_small": [2, 5, 8, 11],
+            "vit_base": [2, 5, 8, 11],
+            "vit_large": [4, 11, 17, 23],
+            "vit_giant2": [9, 19, 29, 39],
+        }[arch_name]
+    else:
+        assert layers == 1
+        out_index = [layer_count - 1]
+
+    model = DepthEncoderDecoder(backbone=backbone, decode_head=linear_depth_head)
+    model.backbone.forward = partial(
+        backbone.get_intermediate_layers,
+        n=out_index,
+        reshape=True,
+        return_class_token=True,
+        norm=False,
+    )
+    model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(patch_size)(x[0]))
+
+    if pretrained:
+        layers_str = str(layers) if layers == 4 else ""
+        weights_str = weights.value.lower()
+        url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_linear{layers_str}_head.pth"
+        checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        if "state_dict" in checkpoint:
+            state_dict = checkpoint["state_dict"]
+        model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+def dinov2_vits14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_linear_depther(
+        arch_name="vit_small", layers=layers, pretrained=pretrained, weights=weights, **kwargs
+    )
+
+
+def dinov2_vitb14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_linear_depther(
+        arch_name="vit_base", layers=layers, pretrained=pretrained, weights=weights, **kwargs
+    )
+
+
+def dinov2_vitl14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_linear_depther(
+        arch_name="vit_large", layers=layers, pretrained=pretrained, weights=weights, **kwargs
+    )
+
+
+def dinov2_vitg14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_linear_depther(
+        arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs
+    )
+
+
+def _make_dinov2_dpt_depth_head(*, embed_dim: int, min_depth: float, max_depth: float):
+    return DPTHead(
+        in_channels=[embed_dim] * 4,
+        channels=256,
+        embed_dims=embed_dim,
+        post_process_channels=[embed_dim // 2 ** (3 - i) for i in range(4)],
+        readout_type="project",
+        min_depth=min_depth,
+        max_depth=max_depth,
+        loss_decode=(),
+    )
+
+
+def _make_dinov2_dpt_depther(
+    *,
+    arch_name: str = "vit_large",
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.NYU,
+    depth_range: Optional[Tuple[float, float]] = None,
+    **kwargs,
+):
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    if depth_range is None:
+        depth_range = _get_depth_range(pretrained, weights)
+    min_depth, max_depth = depth_range
+
+    backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
+
+    model_name = _make_dinov2_model_name(arch_name, backbone.patch_size)
+    dpt_depth_head = _make_dinov2_dpt_depth_head(embed_dim=backbone.embed_dim, min_depth=min_depth, max_depth=max_depth)
+
+    out_index = {
+        "vit_small": [2, 5, 8, 11],
+        "vit_base": [2, 5, 8, 11],
+        "vit_large": [4, 11, 17, 23],
+        "vit_giant2": [9, 19, 29, 39],
+    }[arch_name]
+
+    model = DepthEncoderDecoder(backbone=backbone, decode_head=dpt_depth_head)
+    model.backbone.forward = partial(
+        backbone.get_intermediate_layers,
+        n=out_index,
+        reshape=True,
+        return_class_token=True,
+        norm=False,
+    )
+    model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone.patch_size)(x[0]))
+
+    if pretrained:
+        weights_str = weights.value.lower()
+        url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_dpt_head.pth"
+        checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        if "state_dict" in checkpoint:
+            state_dict = checkpoint["state_dict"]
+        model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+def dinov2_vits14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_dpt_depther(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitb14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_dpt_depther(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitl14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_dpt_depther(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitg14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_dpt_depther(
+        arch_name="vit_giant2", ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs
+    )
diff --git a/src/dinov2/hub/utils.py b/src/dinov2/hub/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c6641404093652d5a2f19b4cf283d976ec39e64
--- /dev/null
+++ b/src/dinov2/hub/utils.py
@@ -0,0 +1,39 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import itertools
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+_DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2"
+
+
+def _make_dinov2_model_name(arch_name: str, patch_size: int, num_register_tokens: int = 0) -> str:
+    compact_arch_name = arch_name.replace("_", "")[:4]
+    registers_suffix = f"_reg{num_register_tokens}" if num_register_tokens else ""
+    return f"dinov2_{compact_arch_name}{patch_size}{registers_suffix}"
+
+
+class CenterPadding(nn.Module):
+    def __init__(self, multiple):
+        super().__init__()
+        self.multiple = multiple
+
+    def _get_pad(self, size):
+        new_size = math.ceil(size / self.multiple) * self.multiple
+        pad_size = new_size - size
+        pad_size_left = pad_size // 2
+        pad_size_right = pad_size - pad_size_left
+        return pad_size_left, pad_size_right
+
+    @torch.inference_mode()
+    def forward(self, x):
+        pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))
+        output = F.pad(x, pads)
+        return output
diff --git a/src/dinov2/layers/__init__.py b/src/dinov2/layers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3317a482f21ee3e926958364d24ab2185cdf07da
--- /dev/null
+++ b/src/dinov2/layers/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .dino_head import DINOHead
+from .mlp import Mlp
+from .patch_embed import PatchEmbed
+from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
+from .block import NestedTensorBlock
+from .block_vis import NestedTensorBlock as NestedTensorBlockVis
+from .attention import MemEffAttention
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diff --git a/src/dinov2/layers/__pycache__/swiglu_ffn.cpython-310.pyc b/src/dinov2/layers/__pycache__/swiglu_ffn.cpython-310.pyc
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diff --git a/src/dinov2/layers/attention.py b/src/dinov2/layers/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1ece26a0d24c29359fcb1c3bc78aa731ff10adf
--- /dev/null
+++ b/src/dinov2/layers/attention.py
@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+import logging
+import os
+import warnings
+
+from torch import Tensor
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+try:
+    if XFORMERS_ENABLED:
+        from xformers.ops import memory_efficient_attention, unbind
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (Attention)")
+    else:
+        warnings.warn("xFormers is disabled (Attention)")
+        raise ImportError
+except ImportError:
+    XFORMERS_AVAILABLE = False
+    warnings.warn("xFormers is not available (Attention)")
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+
+        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(Attention):
+    def forward(self, x: Tensor, attn_bias=None) -> Tensor:
+        if not False:
+            if attn_bias is not None:
+                raise AssertionError("xFormers is required for using nested tensors")
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
diff --git a/src/dinov2/layers/block.py b/src/dinov2/layers/block.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ff4fded00243b70c6690bb367026013e9e30b12
--- /dev/null
+++ b/src/dinov2/layers/block.py
@@ -0,0 +1,288 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+import logging
+import os
+from typing import Callable, List, Any, Tuple, Dict
+import warnings
+
+import torch
+from torch import nn, Tensor
+
+from .attention import Attention, MemEffAttention
+from .drop_path import DropPath
+from .layer_scale import LayerScale
+from .mlp import Mlp
+
+
+logger = logging.getLogger("dinov2")
+
+
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+try:
+    if XFORMERS_ENABLED:
+        from xformers.ops import fmha, scaled_index_add, index_select_cat
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (Block)")
+    else:
+        warnings.warn("xFormers is disabled (Block)")
+        raise ImportError
+except ImportError:
+    XFORMERS_AVAILABLE = False
+
+    warnings.warn("xFormers is not available (Block)")
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        ffn_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values=None,
+        drop_path: float = 0.0,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        attn_class: Callable[..., nn.Module] = Attention,
+        ffn_layer: Callable[..., nn.Module] = Mlp,
+    ) -> None:
+        super().__init__()
+        # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_class(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            proj_bias=proj_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+            bias=ffn_bias,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.sample_drop_ratio = drop_path
+
+    def forward(self, x: Tensor) -> Tensor:
+        def attn_residual_func(x: Tensor) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x)))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+
+        if self.training and self.sample_drop_ratio > 0.1:
+            # the overhead is compensated only for a drop path rate larger than 0.1
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+        elif self.training and self.sample_drop_ratio > 0.0:
+            x = x + self.drop_path1(attn_residual_func(x))
+            x = x + self.drop_path1(ffn_residual_func(x))  # FIXME: drop_path2
+        else:
+            x = x + attn_residual_func(x)
+            x = x + ffn_residual_func(x)
+        return x
+
+
+def drop_add_residual_stochastic_depth(
+    x: Tensor,
+    residual_func: Callable[[Tensor], Tensor],
+    sample_drop_ratio: float = 0.0,
+) -> Tensor:
+    # 1) extract subset using permutation
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    x_subset = x[brange]
+
+    # 2) apply residual_func to get residual
+    residual = residual_func(x_subset)
+
+    x_flat = x.flatten(1)
+    residual = residual.flatten(1)
+
+    residual_scale_factor = b / sample_subset_size
+
+    # 3) add the residual
+    x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    return x_plus_residual.view_as(x)
+
+
+def get_branges_scales(x, sample_drop_ratio=0.0):
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    residual_scale_factor = b / sample_subset_size
+    return brange, residual_scale_factor
+
+
+def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
+    if scaling_vector is None:
+        x_flat = x.flatten(1)
+        residual = residual.flatten(1)
+        x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    else:
+        x_plus_residual = scaled_index_add(
+            x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
+        )
+    return x_plus_residual
+
+
+attn_bias_cache: Dict[Tuple, Any] = {}
+
+
+def get_attn_bias_and_cat(x_list, branges=None):
+    """
+    this will perform the index select, cat the tensors, and provide the attn_bias from cache
+    """
+    batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
+    all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
+    if all_shapes not in attn_bias_cache.keys():
+        seqlens = []
+        for b, x in zip(batch_sizes, x_list):
+            for _ in range(b):
+                seqlens.append(x.shape[1])
+        attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
+        attn_bias._batch_sizes = batch_sizes
+        attn_bias_cache[all_shapes] = attn_bias
+
+    if branges is not None:
+        cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
+    else:
+        tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
+        cat_tensors = torch.cat(tensors_bs1, dim=1)
+
+    return attn_bias_cache[all_shapes], cat_tensors
+
+
+def drop_add_residual_stochastic_depth_list(
+    x_list: List[Tensor],
+    residual_func: Callable[[Tensor, Any], Tensor],
+    sample_drop_ratio: float = 0.0,
+    scaling_vector=None,
+) -> Tensor:
+    # 1) generate random set of indices for dropping samples in the batch
+    branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
+    branges = [s[0] for s in branges_scales]
+    residual_scale_factors = [s[1] for s in branges_scales]
+
+    # 2) get attention bias and index+concat the tensors
+    attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
+
+    # 3) apply residual_func to get residual, and split the result
+    residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias))  # type: ignore
+
+    outputs = []
+    for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
+        outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
+    return outputs
+
+
+class NestedTensorBlock(Block):
+    def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
+        """
+        x_list contains a list of tensors to nest together and run
+        """
+        assert isinstance(self.attn, MemEffAttention)
+
+        if self.training and self.sample_drop_ratio > 0.0:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.attn(self.norm1(x), attn_bias=attn_bias)
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.mlp(self.norm2(x))
+
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            return x_list
+        else:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls2(self.mlp(self.norm2(x)))
+
+            attn_bias, x = get_attn_bias_and_cat(x_list)
+            x = x + attn_residual_func(x, attn_bias=attn_bias)
+            x = x + ffn_residual_func(x)
+            return attn_bias.split(x)
+        
+    def forward_2(self, x: Tensor) -> Tensor:
+        def attn_residual_func(x: Tensor) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x)))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+
+        if self.training and self.sample_drop_ratio > 0.1:
+            # the overhead is compensated only for a drop path rate larger than 0.1
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+        elif self.training and self.sample_drop_ratio > 0.0:
+            x = x + self.drop_path1(attn_residual_func(x))
+            x = x + self.drop_path1(ffn_residual_func(x))  # FIXME: drop_path2
+        else:
+            x = x + attn_residual_func(x)
+            x = x + ffn_residual_func(x)
+        return x
+
+    def forward(self, x_or_x_list):
+        if isinstance(x_or_x_list, Tensor):
+            assert isinstance(x_or_x_list, torch.Tensor), "Expected a torch.Tensor"
+            return super().forward(x_or_x_list)
+        elif isinstance(x_or_x_list, list):
+            if not XFORMERS_AVAILABLE:
+                raise AssertionError("xFormers is required for using nested tensors")
+            return self.forward_nested(x_or_x_list)
+        else:
+            raise AssertionError
diff --git a/src/dinov2/layers/block_vis.py b/src/dinov2/layers/block_vis.py
new file mode 100644
index 0000000000000000000000000000000000000000..61d510496992549439a2193a77ec28064da5d2a2
--- /dev/null
+++ b/src/dinov2/layers/block_vis.py
@@ -0,0 +1,267 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+import logging
+import os
+from typing import Callable, List, Any, Tuple, Dict
+import warnings
+
+import torch
+from torch import nn, Tensor
+
+from .attention import Attention, MemEffAttention
+from .drop_path import DropPath
+from .layer_scale import LayerScale
+from .mlp import Mlp
+
+
+logger = logging.getLogger("dinov2")
+
+
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+try:
+    if XFORMERS_ENABLED:
+        from xformers.ops import fmha, scaled_index_add, index_select_cat
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (Block)")
+    else:
+        warnings.warn("xFormers is disabled (Block)")
+        raise ImportError
+except ImportError:
+    XFORMERS_AVAILABLE = False
+
+    warnings.warn("xFormers is not available (Block)")
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        ffn_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values=None,
+        drop_path: float = 0.0,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        attn_class: Callable[..., nn.Module] = Attention,
+        ffn_layer: Callable[..., nn.Module] = Mlp,
+    ) -> None:
+        super().__init__()
+        # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_class(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            proj_bias=proj_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+            bias=ffn_bias,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.sample_drop_ratio = drop_path
+
+    def forward(self, x: Tensor, return_attention=False) -> Tensor:
+        def attn_residual_func(x: Tensor) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x)))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+        
+        # Add this 2 lines
+        if return_attention:
+            return self.attn(self.norm1(x), return_attn=True)
+            
+        if self.training and self.sample_drop_ratio > 0.1:
+            # the overhead is compensated only for a drop path rate larger than 0.1
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+        elif self.training and self.sample_drop_ratio > 0.0:
+            x = x + self.drop_path1(attn_residual_func(x))
+            x = x + self.drop_path1(ffn_residual_func(x))  # FIXME: drop_path2
+        else:
+            x = x + attn_residual_func(x)
+            x = x + ffn_residual_func(x)
+        return x
+
+
+
+def drop_add_residual_stochastic_depth(
+    x: Tensor,
+    residual_func: Callable[[Tensor], Tensor],
+    sample_drop_ratio: float = 0.0,
+) -> Tensor:
+    # 1) extract subset using permutation
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    x_subset = x[brange]
+
+    # 2) apply residual_func to get residual
+    residual = residual_func(x_subset)
+
+    x_flat = x.flatten(1)
+    residual = residual.flatten(1)
+
+    residual_scale_factor = b / sample_subset_size
+
+    # 3) add the residual
+    x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    return x_plus_residual.view_as(x)
+
+
+def get_branges_scales(x, sample_drop_ratio=0.0):
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    residual_scale_factor = b / sample_subset_size
+    return brange, residual_scale_factor
+
+
+def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
+    if scaling_vector is None:
+        x_flat = x.flatten(1)
+        residual = residual.flatten(1)
+        x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    else:
+        x_plus_residual = scaled_index_add(
+            x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
+        )
+    return x_plus_residual
+
+
+attn_bias_cache: Dict[Tuple, Any] = {}
+
+
+def get_attn_bias_and_cat(x_list, branges=None):
+    """
+    this will perform the index select, cat the tensors, and provide the attn_bias from cache
+    """
+    batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
+    all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
+    if all_shapes not in attn_bias_cache.keys():
+        seqlens = []
+        for b, x in zip(batch_sizes, x_list):
+            for _ in range(b):
+                seqlens.append(x.shape[1])
+        attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
+        attn_bias._batch_sizes = batch_sizes
+        attn_bias_cache[all_shapes] = attn_bias
+
+    if branges is not None:
+        cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
+    else:
+        tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
+        cat_tensors = torch.cat(tensors_bs1, dim=1)
+
+    return attn_bias_cache[all_shapes], cat_tensors
+
+
+def drop_add_residual_stochastic_depth_list(
+    x_list: List[Tensor],
+    residual_func: Callable[[Tensor, Any], Tensor],
+    sample_drop_ratio: float = 0.0,
+    scaling_vector=None,
+) -> Tensor:
+    # 1) generate random set of indices for dropping samples in the batch
+    branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
+    branges = [s[0] for s in branges_scales]
+    residual_scale_factors = [s[1] for s in branges_scales]
+
+    # 2) get attention bias and index+concat the tensors
+    attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
+
+    # 3) apply residual_func to get residual, and split the result
+    residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias))  # type: ignore
+
+    outputs = []
+    for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
+        outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
+    return outputs
+
+
+class NestedTensorBlock(Block):
+    def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
+        """
+        x_list contains a list of tensors to nest together and run
+        """
+        assert isinstance(self.attn, MemEffAttention)
+
+        if self.training and self.sample_drop_ratio > 0.0:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.attn(self.norm1(x), attn_bias=attn_bias)
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.mlp(self.norm2(x))
+
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            return x_list
+        else:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls2(self.mlp(self.norm2(x)))
+
+            attn_bias, x = get_attn_bias_and_cat(x_list)
+            x = x + attn_residual_func(x, attn_bias=attn_bias)
+            x = x + ffn_residual_func(x)
+            return attn_bias.split(x)
+
+    def forward(self, x_or_x_list, return_attention=False):
+        if isinstance(x_or_x_list, Tensor):
+            # Change the following line
+            # return super().forward(x_or_x_list)
+            return super().forward(x_or_x_list, return_attention)
+        elif isinstance(x_or_x_list, list):
+            assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
+            return self.forward_nested(x_or_x_list)
+        else:
+            raise AssertionError
+
diff --git a/src/dinov2/layers/dino_head.py b/src/dinov2/layers/dino_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ace8ffd6297a1dd480b19db407b662a6ea0f565
--- /dev/null
+++ b/src/dinov2/layers/dino_head.py
@@ -0,0 +1,58 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from torch.nn.init import trunc_normal_
+from torch.nn.utils import weight_norm
+
+
+class DINOHead(nn.Module):
+    def __init__(
+        self,
+        in_dim,
+        out_dim,
+        use_bn=False,
+        nlayers=3,
+        hidden_dim=2048,
+        bottleneck_dim=256,
+        mlp_bias=True,
+    ):
+        super().__init__()
+        nlayers = max(nlayers, 1)
+        self.mlp = _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=hidden_dim, use_bn=use_bn, bias=mlp_bias)
+        self.apply(self._init_weights)
+        self.last_layer = weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False))
+        self.last_layer.weight_g.data.fill_(1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.mlp(x)
+        eps = 1e-6 if x.dtype == torch.float16 else 1e-12
+        x = nn.functional.normalize(x, dim=-1, p=2, eps=eps)
+        x = self.last_layer(x)
+        return x
+
+
+def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True):
+    if nlayers == 1:
+        return nn.Linear(in_dim, bottleneck_dim, bias=bias)
+    else:
+        layers = [nn.Linear(in_dim, hidden_dim, bias=bias)]
+        if use_bn:
+            layers.append(nn.BatchNorm1d(hidden_dim))
+        layers.append(nn.GELU())
+        for _ in range(nlayers - 2):
+            layers.append(nn.Linear(hidden_dim, hidden_dim, bias=bias))
+            if use_bn:
+                layers.append(nn.BatchNorm1d(hidden_dim))
+            layers.append(nn.GELU())
+        layers.append(nn.Linear(hidden_dim, bottleneck_dim, bias=bias))
+        return nn.Sequential(*layers)
diff --git a/src/dinov2/layers/drop_path.py b/src/dinov2/layers/drop_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d640e0b969b8dcba96260243473700b4e5b24b5
--- /dev/null
+++ b/src/dinov2/layers/drop_path.py
@@ -0,0 +1,34 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
+
+
+from torch import nn
+
+
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0:
+        random_tensor.div_(keep_prob)
+    output = x * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
diff --git a/src/dinov2/layers/layer_scale.py b/src/dinov2/layers/layer_scale.py
new file mode 100644
index 0000000000000000000000000000000000000000..51df0d7ce61f2b41fa9e6369f52391dd7fe7d386
--- /dev/null
+++ b/src/dinov2/layers/layer_scale.py
@@ -0,0 +1,27 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
+
+from typing import Union
+
+import torch
+from torch import Tensor
+from torch import nn
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: Union[float, Tensor] = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
diff --git a/src/dinov2/layers/mlp.py b/src/dinov2/layers/mlp.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbf9432aae9258612caeae910a7bde17999e328e
--- /dev/null
+++ b/src/dinov2/layers/mlp.py
@@ -0,0 +1,40 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py
+
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
diff --git a/src/dinov2/layers/patch_embed.py b/src/dinov2/layers/patch_embed.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b7c0804784a42cf80c0297d110dcc68cc85b339
--- /dev/null
+++ b/src/dinov2/layers/patch_embed.py
@@ -0,0 +1,88 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+import torch.nn as nn
+
+
+def make_2tuple(x):
+    if isinstance(x, tuple):
+        assert len(x) == 2
+        return x
+
+    assert isinstance(x, int)
+    return (x, x)
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D image to patch embedding: (B,C,H,W) -> (B,N,D)
+
+    Args:
+        img_size: Image size.
+        patch_size: Patch token size.
+        in_chans: Number of input image channels.
+        embed_dim: Number of linear projection output channels.
+        norm_layer: Normalization layer.
+    """
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer: Optional[Callable] = None,
+        flatten_embedding: bool = True,
+    ) -> None:
+        super().__init__()
+
+        image_HW = make_2tuple(img_size)
+        patch_HW = make_2tuple(patch_size)
+        patch_grid_size = (
+            image_HW[0] // patch_HW[0],
+            image_HW[1] // patch_HW[1],
+        )
+
+        self.img_size = image_HW
+        self.patch_size = patch_HW
+        self.patches_resolution = patch_grid_size
+        self.num_patches = patch_grid_size[0] * patch_grid_size[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.flatten_embedding = flatten_embedding
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x: Tensor) -> Tensor:
+        _, _, H, W = x.shape
+        patch_H, patch_W = self.patch_size
+
+        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+
+        x = self.proj(x)  # B C H W
+        H, W = x.size(2), x.size(3)
+        x = x.flatten(2).transpose(1, 2)  # B HW C
+        x = self.norm(x)
+        if not self.flatten_embedding:
+            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
+        return x
+
+    def flops(self) -> float:
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
diff --git a/src/dinov2/layers/swiglu_ffn.py b/src/dinov2/layers/swiglu_ffn.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e9dafa4592a408f6874d54853e8f60db5c41f74
--- /dev/null
+++ b/src/dinov2/layers/swiglu_ffn.py
@@ -0,0 +1,72 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import os
+from typing import Callable, Optional
+import warnings
+
+from torch import Tensor, nn
+import torch.nn.functional as F
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
+        self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+
+
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+try:
+    if XFORMERS_ENABLED:
+        from xformers.ops import SwiGLU
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (SwiGLU)")
+    else:
+        warnings.warn("xFormers is disabled (SwiGLU)")
+        raise ImportError
+except ImportError:
+    SwiGLU = SwiGLUFFN
+    XFORMERS_AVAILABLE = False
+
+    warnings.warn("xFormers is not available (SwiGLU)")
+
+
+class SwiGLUFFNFused(SwiGLU):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+        super().__init__(
+            in_features=in_features,
+            hidden_features=hidden_features,
+            out_features=out_features,
+            bias=bias,
+        )
diff --git a/src/dinov2/logging/__init__.py b/src/dinov2/logging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..04a7f02204316d4d1ef38bf6080dae3d66241c25
--- /dev/null
+++ b/src/dinov2/logging/__init__.py
@@ -0,0 +1,102 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import functools
+import logging
+import os
+import sys
+from typing import Optional
+
+import dinov2.distributed as distributed
+from .helpers import MetricLogger, SmoothedValue
+
+
+# So that calling _configure_logger multiple times won't add many handlers
+@functools.lru_cache()
+def _configure_logger(
+    name: Optional[str] = None,
+    *,
+    level: int = logging.DEBUG,
+    output: Optional[str] = None,
+):
+    """
+    Configure a logger.
+
+    Adapted from Detectron2.
+
+    Args:
+        name: The name of the logger to configure.
+        level: The logging level to use.
+        output: A file name or a directory to save log. If None, will not save log file.
+            If ends with ".txt" or ".log", assumed to be a file name.
+            Otherwise, logs will be saved to `output/log.txt`.
+
+    Returns:
+        The configured logger.
+    """
+
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    logger.propagate = False
+
+    # Loosely match Google glog format:
+    #   [IWEF]yyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg
+    # but use a shorter timestamp and include the logger name:
+    #   [IWEF]yyyymmdd hh:mm:ss logger threadid file:line] msg
+    fmt_prefix = "%(levelname).1s%(asctime)s %(process)s %(name)s %(filename)s:%(lineno)s] "
+    fmt_message = "%(message)s"
+    fmt = fmt_prefix + fmt_message
+    datefmt = "%Y%m%d %H:%M:%S"
+    formatter = logging.Formatter(fmt=fmt, datefmt=datefmt)
+
+    # stdout logging for main worker only
+    if distributed.is_main_process():
+        handler = logging.StreamHandler(stream=sys.stdout)
+        handler.setLevel(logging.DEBUG)
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+
+    # file logging for all workers
+    if output:
+        if os.path.splitext(output)[-1] in (".txt", ".log"):
+            filename = output
+        else:
+            filename = os.path.join(output, "logs", "log.txt")
+
+        if not distributed.is_main_process():
+            global_rank = distributed.get_global_rank()
+            filename = filename + ".rank{}".format(global_rank)
+
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+
+        handler = logging.StreamHandler(open(filename, "a"))
+        handler.setLevel(logging.DEBUG)
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+
+    return logger
+
+
+def setup_logging(
+    output: Optional[str] = None,
+    *,
+    name: Optional[str] = None,
+    level: int = logging.DEBUG,
+    capture_warnings: bool = True,
+) -> None:
+    """
+    Setup logging.
+
+    Args:
+        output: A file name or a directory to save log files. If None, log
+            files will not be saved. If output ends with ".txt" or ".log", it
+            is assumed to be a file name.
+            Otherwise, logs will be saved to `output/log.txt`.
+        name: The name of the logger to configure, by default the root logger.
+        level: The logging level to use.
+        capture_warnings: Whether warnings should be captured as logs.
+    """
+    logging.captureWarnings(capture_warnings)
+    _configure_logger(name, level=level, output=output)
diff --git a/src/dinov2/logging/helpers.py b/src/dinov2/logging/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6e70bb15505cbbc4c4732b069ee919bf921a74f
--- /dev/null
+++ b/src/dinov2/logging/helpers.py
@@ -0,0 +1,194 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from collections import defaultdict, deque
+import datetime
+import json
+import logging
+import time
+
+import torch
+
+import dinov2.distributed as distributed
+
+
+logger = logging.getLogger("dinov2")
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t", output_file=None):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+        self.output_file = output_file
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append("{}: {}".format(name, str(meter)))
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def dump_in_output_file(self, iteration, iter_time, data_time):
+        if self.output_file is None or not distributed.is_main_process():
+            return
+        dict_to_dump = dict(
+            iteration=iteration,
+            iter_time=iter_time,
+            data_time=data_time,
+        )
+        dict_to_dump.update({k: v.median for k, v in self.meters.items()})
+        with open(self.output_file, "a") as f:
+            f.write(json.dumps(dict_to_dump) + "\n")
+        pass
+
+    def log_every(self, iterable, print_freq, header=None, n_iterations=None, start_iteration=0):
+        i = start_iteration
+        if not header:
+            header = ""
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt="{avg:.6f}")
+        data_time = SmoothedValue(fmt="{avg:.6f}")
+
+        if n_iterations is None:
+            n_iterations = len(iterable)
+
+        space_fmt = ":" + str(len(str(n_iterations))) + "d"
+
+        log_list = [
+            header,
+            "[{0" + space_fmt + "}/{1}]",
+            "eta: {eta}",
+            "{meters}",
+            "time: {time}",
+            "data: {data}",
+        ]
+        if torch.cuda.is_available():
+            log_list += ["max mem: {memory:.0f}"]
+
+        log_msg = self.delimiter.join(log_list)
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == n_iterations - 1:
+                self.dump_in_output_file(iteration=i, iter_time=iter_time.avg, data_time=data_time.avg)
+                eta_seconds = iter_time.global_avg * (n_iterations - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    logger.info(
+                        log_msg.format(
+                            i,
+                            n_iterations,
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                            memory=torch.cuda.max_memory_allocated() / MB,
+                        )
+                    )
+                else:
+                    logger.info(
+                        log_msg.format(
+                            i,
+                            n_iterations,
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                        )
+                    )
+            i += 1
+            end = time.time()
+            if i >= n_iterations:
+                break
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        logger.info("{} Total time: {} ({:.6f} s / it)".format(header, total_time_str, total_time / n_iterations))
+
+
+class SmoothedValue:
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, num=1):
+        self.deque.append(value)
+        self.count += num
+        self.total += value * num
+
+    def synchronize_between_processes(self):
+        """
+        Distributed synchronization of the metric
+        Warning: does not synchronize the deque!
+        """
+        if not distributed.is_enabled():
+            return
+        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
+        torch.distributed.barrier()
+        torch.distributed.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median,
+            avg=self.avg,
+            global_avg=self.global_avg,
+            max=self.max,
+            value=self.value,
+        )
diff --git a/src/dinov2/loss/__init__.py b/src/dinov2/loss/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6b0115b74edbd74b324c9056a57fade363c58fd
--- /dev/null
+++ b/src/dinov2/loss/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .dino_clstoken_loss import DINOLoss
+from .ibot_patch_loss import iBOTPatchLoss
+from .koleo_loss import KoLeoLoss
diff --git a/src/dinov2/loss/dino_clstoken_loss.py b/src/dinov2/loss/dino_clstoken_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..c31808e36e6c38ee6dae13ba0443bf1946242117
--- /dev/null
+++ b/src/dinov2/loss/dino_clstoken_loss.py
@@ -0,0 +1,99 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import nn
+
+
+class DINOLoss(nn.Module):
+    def __init__(
+        self,
+        out_dim,
+        student_temp=0.1,
+        center_momentum=0.9,
+    ):
+        super().__init__()
+        self.student_temp = student_temp
+        self.center_momentum = center_momentum
+        self.register_buffer("center", torch.zeros(1, out_dim))
+        self.updated = True
+        self.reduce_handle = None
+        self.len_teacher_output = None
+        self.async_batch_center = None
+
+    @torch.no_grad()
+    def softmax_center_teacher(self, teacher_output, teacher_temp):
+        self.apply_center_update()
+        # teacher centering and sharpening
+        return F.softmax((teacher_output - self.center) / teacher_temp, dim=-1)
+
+    @torch.no_grad()
+    def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_iterations=3):
+        teacher_output = teacher_output.float()
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        Q = torch.exp(teacher_output / teacher_temp).t()  # Q is K-by-B for consistency with notations from our paper
+        B = Q.shape[1] * world_size  # number of samples to assign
+        K = Q.shape[0]  # how many prototypes
+
+        # make the matrix sums to 1
+        sum_Q = torch.sum(Q)
+        if dist.is_initialized():
+            dist.all_reduce(sum_Q)
+        Q /= sum_Q
+
+        for it in range(n_iterations):
+            # normalize each row: total weight per prototype must be 1/K
+            sum_of_rows = torch.sum(Q, dim=1, keepdim=True)
+            if dist.is_initialized():
+                dist.all_reduce(sum_of_rows)
+            Q /= sum_of_rows
+            Q /= K
+
+            # normalize each column: total weight per sample must be 1/B
+            Q /= torch.sum(Q, dim=0, keepdim=True)
+            Q /= B
+
+        Q *= B  # the columns must sum to 1 so that Q is an assignment
+        return Q.t()
+
+    def forward(self, student_output_list, teacher_out_softmaxed_centered_list):
+        """
+        Cross-entropy between softmax outputs of the teacher and student networks.
+        """
+        # TODO: Use cross_entropy_distribution here
+        total_loss = 0
+        for s in student_output_list:
+            lsm = F.log_softmax(s / self.student_temp, dim=-1)
+            for t in teacher_out_softmaxed_centered_list:
+                loss = torch.sum(t * lsm, dim=-1)
+                total_loss -= loss.mean()
+        return total_loss
+
+    @torch.no_grad()
+    def update_center(self, teacher_output):
+        self.reduce_center_update(teacher_output)
+
+    @torch.no_grad()
+    def reduce_center_update(self, teacher_output):
+        self.updated = False
+        self.len_teacher_output = len(teacher_output)
+        self.async_batch_center = torch.sum(teacher_output, dim=0, keepdim=True)
+        if dist.is_initialized():
+            self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True)
+
+    @torch.no_grad()
+    def apply_center_update(self):
+        if self.updated is False:
+            world_size = dist.get_world_size() if dist.is_initialized() else 1
+
+            if self.reduce_handle is not None:
+                self.reduce_handle.wait()
+            _t = self.async_batch_center / (self.len_teacher_output * world_size)
+
+            self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum)
+
+            self.updated = True
diff --git a/src/dinov2/loss/ibot_patch_loss.py b/src/dinov2/loss/ibot_patch_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..6732cda0c311c69f193669ebc950fc8665871442
--- /dev/null
+++ b/src/dinov2/loss/ibot_patch_loss.py
@@ -0,0 +1,151 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import nn
+
+import logging
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import cross_entropy
+
+    def lossfunc(t, s, temp):
+        s = s.float()
+        t = t.float()
+        if s.ndim == 2:
+            return -cross_entropy(s.unsqueeze(0), t.unsqueeze(0), temp, bw_inplace=True).squeeze(0)
+        elif s.ndim == 3:
+            return -cross_entropy(s, t, temp, bw_inplace=True)
+
+except ImportError:
+
+    def lossfunc(t, s, temp):
+        return torch.sum(t * F.log_softmax(s / temp, dim=-1), dim=-1)
+
+
+class iBOTPatchLoss(nn.Module):
+    def __init__(self, patch_out_dim, student_temp=0.1, center_momentum=0.9):
+        super().__init__()
+        self.student_temp = student_temp
+        self.center_momentum = center_momentum
+        self.register_buffer("center", torch.zeros(1, 1, patch_out_dim))
+        self.updated = True
+        self.reduce_handle = None
+        self.len_teacher_patch_tokens = None
+        self.async_batch_center = None
+
+    @torch.no_grad()
+    def softmax_center_teacher(self, teacher_patch_tokens, teacher_temp):
+        self.apply_center_update()
+        # teacher centering and sharpening
+        #
+        # WARNING:
+        #   as self.center is a float32, everything gets casted to float32 afterwards
+        #
+        # teacher_patch_tokens = teacher_patch_tokens.float()
+        # return F.softmax((teacher_patch_tokens.sub_(self.center.to(teacher_patch_tokens.dtype))).mul_(1 / teacher_temp), dim=-1)
+
+        return F.softmax((teacher_patch_tokens - self.center) / teacher_temp, dim=-1)
+
+        # this is experimental, keep everything in float16 and let's see what happens:
+        # return F.softmax((teacher_patch_tokens.sub_(self.center)) / teacher_temp, dim=-1)
+
+    @torch.no_grad()
+    def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_masked_patches_tensor, n_iterations=3):
+        teacher_output = teacher_output.float()
+        # world_size = dist.get_world_size() if dist.is_initialized() else 1
+        Q = torch.exp(teacher_output / teacher_temp).t()  # Q is K-by-B for consistency with notations from our paper
+        # B = Q.shape[1] * world_size # number of samples to assign
+        B = n_masked_patches_tensor
+        dist.all_reduce(B)
+        K = Q.shape[0]  # how many prototypes
+
+        # make the matrix sums to 1
+        sum_Q = torch.sum(Q)
+        if dist.is_initialized():
+            dist.all_reduce(sum_Q)
+        Q /= sum_Q
+
+        for it in range(n_iterations):
+            # normalize each row: total weight per prototype must be 1/K
+            sum_of_rows = torch.sum(Q, dim=1, keepdim=True)
+            if dist.is_initialized():
+                dist.all_reduce(sum_of_rows)
+            Q /= sum_of_rows
+            Q /= K
+
+            # normalize each column: total weight per sample must be 1/B
+            Q /= torch.sum(Q, dim=0, keepdim=True)
+            Q /= B
+
+        Q *= B  # the columns must sum to 1 so that Q is an assignment
+        return Q.t()
+
+    def forward(self, student_patch_tokens, teacher_patch_tokens, student_masks_flat):
+        """
+        Cross-entropy between softmax outputs of the teacher and student networks.
+        student_patch_tokens: (B, N, D) tensor
+        teacher_patch_tokens: (B, N, D) tensor
+        student_masks_flat: (B, N) tensor
+        """
+        t = teacher_patch_tokens
+        s = student_patch_tokens
+        loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1)
+        loss = torch.sum(loss * student_masks_flat.float(), dim=-1) / student_masks_flat.sum(dim=-1).clamp(min=1.0)
+        return -loss.mean()
+
+    def forward_masked(
+        self,
+        student_patch_tokens_masked,
+        teacher_patch_tokens_masked,
+        student_masks_flat,
+        n_masked_patches=None,
+        masks_weight=None,
+    ):
+        t = teacher_patch_tokens_masked
+        s = student_patch_tokens_masked
+        # loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1)
+        loss = lossfunc(t, s, self.student_temp)
+        if masks_weight is None:
+            masks_weight = (
+                (1 / student_masks_flat.sum(-1).clamp(min=1.0))
+                .unsqueeze(-1)
+                .expand_as(student_masks_flat)[student_masks_flat]
+            )
+        if n_masked_patches is not None:
+            loss = loss[:n_masked_patches]
+        loss = loss * masks_weight
+        return -loss.sum() / student_masks_flat.shape[0]
+
+    @torch.no_grad()
+    def update_center(self, teacher_patch_tokens):
+        self.reduce_center_update(teacher_patch_tokens)
+
+    @torch.no_grad()
+    def reduce_center_update(self, teacher_patch_tokens):
+        self.updated = False
+        self.len_teacher_patch_tokens = len(teacher_patch_tokens)
+        self.async_batch_center = torch.sum(teacher_patch_tokens.mean(1), dim=0, keepdim=True)
+        if dist.is_initialized():
+            self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True)
+
+    @torch.no_grad()
+    def apply_center_update(self):
+        if self.updated is False:
+            world_size = dist.get_world_size() if dist.is_initialized() else 1
+
+            if self.reduce_handle is not None:
+                self.reduce_handle.wait()
+            _t = self.async_batch_center / (self.len_teacher_patch_tokens * world_size)
+
+            self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum)
+
+            self.updated = True
diff --git a/src/dinov2/loss/koleo_loss.py b/src/dinov2/loss/koleo_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5cbcd91e0fc0b857f477b0910f957f02a6c4335
--- /dev/null
+++ b/src/dinov2/loss/koleo_loss.py
@@ -0,0 +1,48 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# import torch.distributed as dist
+
+
+logger = logging.getLogger("dinov2")
+
+
+class KoLeoLoss(nn.Module):
+    """Kozachenko-Leonenko entropic loss regularizer from Sablayrolles et al. - 2018 - Spreading vectors for similarity search"""
+
+    def __init__(self):
+        super().__init__()
+        self.pdist = nn.PairwiseDistance(2, eps=1e-8)
+
+    def pairwise_NNs_inner(self, x):
+        """
+        Pairwise nearest neighbors for L2-normalized vectors.
+        Uses Torch rather than Faiss to remain on GPU.
+        """
+        # parwise dot products (= inverse distance)
+        dots = torch.mm(x, x.t())
+        n = x.shape[0]
+        dots.view(-1)[:: (n + 1)].fill_(-1)  # Trick to fill diagonal with -1
+        # max inner prod -> min distance
+        _, I = torch.max(dots, dim=1)  # noqa: E741
+        return I
+
+    def forward(self, student_output, eps=1e-8):
+        """
+        Args:
+            student_output (BxD): backbone output of student
+        """
+        with torch.cuda.amp.autocast(enabled=False):
+            student_output = F.normalize(student_output, eps=eps, p=2, dim=-1)
+            I = self.pairwise_NNs_inner(student_output)  # noqa: E741
+            distances = self.pdist(student_output, student_output[I])  # BxD, BxD -> B
+            loss = -torch.log(distances + eps).mean()
+        return loss
diff --git a/src/dinov2/models/__init__.py b/src/dinov2/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fdff20badbd5244bf79f16bf18dd2cb73982265
--- /dev/null
+++ b/src/dinov2/models/__init__.py
@@ -0,0 +1,43 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+
+from . import vision_transformer as vits
+
+
+logger = logging.getLogger("dinov2")
+
+
+def build_model(args, only_teacher=False, img_size=224):
+    args.arch = args.arch.removesuffix("_memeff")
+    if "vit" in args.arch:
+        vit_kwargs = dict(
+            img_size=img_size,
+            patch_size=args.patch_size,
+            init_values=args.layerscale,
+            ffn_layer=args.ffn_layer,
+            block_chunks=args.block_chunks,
+            qkv_bias=args.qkv_bias,
+            proj_bias=args.proj_bias,
+            ffn_bias=args.ffn_bias,
+            num_register_tokens=args.num_register_tokens,
+            interpolate_offset=args.interpolate_offset,
+            interpolate_antialias=args.interpolate_antialias,
+        )
+        teacher = vits.__dict__[args.arch](**vit_kwargs)
+        if only_teacher:
+            return teacher, teacher.embed_dim
+        student = vits.__dict__[args.arch](
+            **vit_kwargs,
+            drop_path_rate=args.drop_path_rate,
+            drop_path_uniform=args.drop_path_uniform,
+        )
+        embed_dim = student.embed_dim
+    return student, teacher, embed_dim
+
+
+def build_model_from_cfg(cfg, only_teacher=False):
+    return build_model(cfg.student, only_teacher=only_teacher, img_size=cfg.crops.global_crops_size)
diff --git a/src/dinov2/models/__pycache__/__init__.cpython-310.pyc b/src/dinov2/models/__pycache__/__init__.cpython-310.pyc
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diff --git a/src/dinov2/models/__pycache__/vision_transformer.cpython-310.pyc b/src/dinov2/models/__pycache__/vision_transformer.cpython-310.pyc
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diff --git a/src/dinov2/models/__pycache__/vision_transformer_vis.cpython-310.pyc b/src/dinov2/models/__pycache__/vision_transformer_vis.cpython-310.pyc
new file mode 100644
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diff --git a/src/dinov2/models/vision_transformer.py b/src/dinov2/models/vision_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d737be71dd3c58f0fea136a7c1c104e4254699c4
--- /dev/null
+++ b/src/dinov2/models/vision_transformer.py
@@ -0,0 +1,415 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+from functools import partial
+import math
+import logging
+from typing import Sequence, Tuple, Union, Callable
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn.init import trunc_normal_
+
+from src.dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
+
+
+logger = logging.getLogger("dinov2")
+
+
+def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = ".".join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+class BlockChunk(nn.ModuleList):
+    def forward(self, x):
+        for b in self:
+            x = b(x)
+        return x
+
+
+class DinoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=518,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        ffn_bias=True,
+        proj_bias=True,
+        drop_path_rate=0.0,
+        drop_path_uniform=False,
+        init_values=None,  # for layerscale: None or 0 => no layerscale
+        embed_layer=PatchEmbed,
+        act_layer=nn.GELU,
+        block_fn=Block,
+        ffn_layer="mlp",
+        block_chunks=1,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            proj_bias (bool): enable bias for proj in attn if True
+            ffn_bias (bool): enable bias for ffn if True
+            drop_path_rate (float): stochastic depth rate
+            drop_path_uniform (bool): apply uniform drop rate across blocks
+            weight_init (str): weight init scheme
+            init_values (float): layer-scale init values
+            embed_layer (nn.Module): patch embedding layer
+            act_layer (nn.Module): MLP activation layer
+            block_fn (nn.Module): transformer block class
+            ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
+            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
+            num_register_tokens: (int) number of extra cls tokens (so-called "registers")
+            interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
+            interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
+        """
+        super().__init__()
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        self.n_blocks = depth
+        self.num_heads = num_heads
+        self.patch_size = patch_size
+        self.num_register_tokens = num_register_tokens
+        self.interpolate_antialias = interpolate_antialias
+        self.interpolate_offset = interpolate_offset
+
+        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        assert num_register_tokens >= 0
+        self.register_tokens = (
+            nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
+        )
+
+        if drop_path_uniform is True:
+            dpr = [drop_path_rate] * depth
+        else:
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+
+        if ffn_layer == "mlp":
+            logger.info("using MLP layer as FFN")
+            ffn_layer = Mlp
+        elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
+            logger.info("using SwiGLU layer as FFN")
+            ffn_layer = SwiGLUFFNFused
+        elif ffn_layer == "identity":
+            logger.info("using Identity layer as FFN")
+
+            def f(*args, **kwargs):
+                return nn.Identity()
+
+            ffn_layer = f
+        else:
+            raise NotImplementedError
+
+        blocks_list = [
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                proj_bias=proj_bias,
+                ffn_bias=ffn_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                ffn_layer=ffn_layer,
+                init_values=init_values,
+            )
+            for i in range(depth)
+        ]
+        if block_chunks > 0:
+            self.chunked_blocks = True
+            chunked_blocks = []
+            chunksize = depth // block_chunks
+            for i in range(0, depth, chunksize):
+                # this is to keep the block index consistent if we chunk the block list
+                chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
+            self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
+        else:
+            self.chunked_blocks = False
+            self.blocks = nn.ModuleList(blocks_list)
+
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Identity()
+
+        self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
+
+        self.init_weights()
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+        nn.init.normal_(self.cls_token, std=1e-6)
+        if self.register_tokens is not None:
+            nn.init.normal_(self.register_tokens, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        previous_dtype = x.dtype
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        pos_embed = self.pos_embed.float()
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        M = int(math.sqrt(N))  # Recover the number of patches in each dimension
+        assert N == M * M
+        kwargs = {}
+        if self.interpolate_offset:
+            # Historical kludge: add a small number to avoid floating point error in the interpolation, see https://github.com/facebookresearch/dino/issues/8
+            # Note: still needed for backward-compatibility, the underlying operators are using both output size and scale factors
+            sx = float(w0 + self.interpolate_offset) / M
+            sy = float(h0 + self.interpolate_offset) / M
+            kwargs["scale_factor"] = (sx, sy)
+        else:
+            # Simply specify an output size instead of a scale factor
+            kwargs["size"] = (w0, h0)
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, M, M, dim).permute(0, 3, 1, 2),
+            mode="bicubic",
+            antialias=self.interpolate_antialias,
+            **kwargs,
+        )
+        assert (w0, h0) == patch_pos_embed.shape[-2:]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
+
+    def prepare_tokens_with_masks(self, x, masks=None, prompt=None):
+        # print("prompt", prompt)
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)
+        
+        if masks is not None:
+            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
+
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        
+        x = x + self.interpolate_pos_encoding(x, w, h)
+        
+        # if prompt is not None:
+        #     x = torch.cat([x, prompt], dim=1)
+
+        if prompt is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    prompt,
+                    x[:, 1:]
+                ),
+                dim=1
+            )
+
+        if self.register_tokens is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    self.register_tokens.expand(x.shape[0], -1, -1),
+                    x[:, 1:],
+                ),
+                dim=1,
+            )
+
+        return x
+
+    def forward_features_list(self, x_list, masks_list, prompt_list):
+        x = [self.prepare_tokens_with_masks(x, masks, prompt) for x, masks, prompt in zip(x_list, masks_list, prompt_list)]
+        for blk in self.blocks:
+            x = blk(x)
+
+        all_x = x
+        output = []
+        for x, masks in zip(all_x, masks_list):
+            x_norm = self.norm(x)
+            output.append(
+                {
+                    "x_norm_clstoken": x_norm[:, 0],
+                    "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+                    "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+                    "x_prenorm": x,
+                    "masks": masks,
+                }
+            )
+        return output
+
+    def forward_features(self, x, masks=None, prompt=None):
+        # print("forward_features prompt: ", prompt)
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks, prompt)
+
+        x = self.prepare_tokens_with_masks(x, masks, prompt)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x_norm = self.norm(x)
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+            "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+            "x_prenorm": x,
+            "masks": masks,
+        }
+
+    def _get_intermediate_layers_not_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        # If n is an int, take the n last blocks. If it's a list, take them
+        output, total_block_len = [], len(self.blocks)
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in blocks_to_take:
+                output.append(x)
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def _get_intermediate_layers_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        output, i, total_block_len = [], 0, len(self.blocks[-1])
+        # If n is an int, take the n last blocks. If it's a list, take them
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for block_chunk in self.blocks:
+            for blk in block_chunk[i:]:  # Passing the nn.Identity()
+                x = blk(x)
+                if i in blocks_to_take:
+                    output.append(x)
+                i += 1
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,  # Layers or n last layers to take
+        reshape: bool = False,
+        return_class_token: bool = False,
+        norm=True,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        if self.chunked_blocks:
+            outputs = self._get_intermediate_layers_chunked(x, n)
+        else:
+            outputs = self._get_intermediate_layers_not_chunked(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        class_tokens = [out[:, 0] for out in outputs]
+        outputs = [out[:, 1 + self.num_register_tokens :] for out in outputs]
+        if reshape:
+            B, _, w, h = x.shape
+            outputs = [
+                out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+        if return_class_token:
+            return tuple(zip(outputs, class_tokens))
+        return tuple(outputs)
+
+    def forward(self, x, is_training=False, prompt=None):
+        ret = self.forward_features(x=x, prompt=prompt)
+        if is_training:
+            return ret
+        else:
+            return self.head(ret["x_norm_clstoken"])
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = ""):
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+
+def vit_small(patch_size=16, num_register_tokens=0, init_values=1.0, block_chunks=0,  **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        init_values=init_values,  # for layerscale: None or 0 => no layerscale
+        block_chunks=block_chunks,
+        **kwargs,
+    )
+    return model
+
+
+def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
+    """
+    Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
+    """
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1536,
+        depth=40,
+        num_heads=24,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
diff --git a/src/dinov2/models/vision_transformer_vis.py b/src/dinov2/models/vision_transformer_vis.py
new file mode 100644
index 0000000000000000000000000000000000000000..e567dd6103a858fb36042fc5f5d31062fd43825c
--- /dev/null
+++ b/src/dinov2/models/vision_transformer_vis.py
@@ -0,0 +1,429 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+from functools import partial
+import math
+import logging
+from typing import Sequence, Tuple, Union, Callable
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn.init import trunc_normal_
+
+from src.dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlockVis as Block
+
+
+logger = logging.getLogger("dinov2")
+
+
+def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = ".".join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+class BlockChunk(nn.ModuleList):
+    def forward(self, x):
+        for b in self:
+            x = b(x)
+        return x
+
+
+class DinoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=518,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        ffn_bias=True,
+        proj_bias=True,
+        drop_path_rate=0.0,
+        drop_path_uniform=False,
+        init_values=None,  # for layerscale: None or 0 => no layerscale
+        embed_layer=PatchEmbed,
+        act_layer=nn.GELU,
+        block_fn=Block,
+        ffn_layer="mlp",
+        block_chunks=1,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            proj_bias (bool): enable bias for proj in attn if True
+            ffn_bias (bool): enable bias for ffn if True
+            drop_path_rate (float): stochastic depth rate
+            drop_path_uniform (bool): apply uniform drop rate across blocks
+            weight_init (str): weight init scheme
+            init_values (float): layer-scale init values
+            embed_layer (nn.Module): patch embedding layer
+            act_layer (nn.Module): MLP activation layer
+            block_fn (nn.Module): transformer block class
+            ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
+            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
+            num_register_tokens: (int) number of extra cls tokens (so-called "registers")
+            interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
+            interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
+        """
+        super().__init__()
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        self.n_blocks = depth
+        self.num_heads = num_heads
+        self.patch_size = patch_size
+        self.num_register_tokens = num_register_tokens
+        self.interpolate_antialias = interpolate_antialias
+        self.interpolate_offset = interpolate_offset
+
+        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        assert num_register_tokens >= 0
+        self.register_tokens = (
+            nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
+        )
+
+        if drop_path_uniform is True:
+            dpr = [drop_path_rate] * depth
+        else:
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+
+        if ffn_layer == "mlp":
+            logger.info("using MLP layer as FFN")
+            ffn_layer = Mlp
+        elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
+            logger.info("using SwiGLU layer as FFN")
+            ffn_layer = SwiGLUFFNFused
+        elif ffn_layer == "identity":
+            logger.info("using Identity layer as FFN")
+
+            def f(*args, **kwargs):
+                return nn.Identity()
+
+            ffn_layer = f
+        else:
+            raise NotImplementedError
+
+        blocks_list = [
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                proj_bias=proj_bias,
+                ffn_bias=ffn_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                ffn_layer=ffn_layer,
+                init_values=init_values,
+            )
+            for i in range(depth)
+        ]
+        if block_chunks > 0:
+            self.chunked_blocks = True
+            chunked_blocks = []
+            chunksize = depth // block_chunks
+            for i in range(0, depth, chunksize):
+                # this is to keep the block index consistent if we chunk the block list
+                chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
+            self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
+        else:
+            self.chunked_blocks = False
+            self.blocks = nn.ModuleList(blocks_list)
+
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Identity()
+
+        self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
+
+        self.init_weights()
+
+    def get_last_self_attention(self, x, masks=None):
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks)
+            
+        x = self.prepare_tokens_with_masks(x, masks)
+        
+        # Run through model, at the last block just return the attention.
+        for i, blk in enumerate(self.blocks):
+            if i < len(self.blocks) - 1:
+                x = blk(x)
+            else: 
+                return blk(x, return_attention=True)
+
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+        nn.init.normal_(self.cls_token, std=1e-6)
+        if self.register_tokens is not None:
+            nn.init.normal_(self.register_tokens, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        previous_dtype = x.dtype
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        pos_embed = self.pos_embed.float()
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        M = int(math.sqrt(N))  # Recover the number of patches in each dimension
+        assert N == M * M
+        kwargs = {}
+        if self.interpolate_offset:
+            # Historical kludge: add a small number to avoid floating point error in the interpolation, see https://github.com/facebookresearch/dino/issues/8
+            # Note: still needed for backward-compatibility, the underlying operators are using both output size and scale factors
+            sx = float(w0 + self.interpolate_offset) / M
+            sy = float(h0 + self.interpolate_offset) / M
+            kwargs["scale_factor"] = (sx, sy)
+        else:
+            # Simply specify an output size instead of a scale factor
+            kwargs["size"] = (w0, h0)
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, M, M, dim).permute(0, 3, 1, 2),
+            mode="bicubic",
+            antialias=self.interpolate_antialias,
+            **kwargs,
+        )
+        assert (w0, h0) == patch_pos_embed.shape[-2:]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
+
+    def prepare_tokens_with_masks(self, x, masks=None, prompt=None):
+        # print("prompt", prompt)
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)
+        
+        if masks is not None:
+            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
+
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        
+        x = x + self.interpolate_pos_encoding(x, w, h)
+        
+        # if prompt is not None:
+        #     x = torch.cat([x, prompt], dim=1)
+
+        if prompt is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    prompt,
+                    x[:, 1:]
+                ),
+                dim=1
+            )
+
+        if self.register_tokens is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    self.register_tokens.expand(x.shape[0], -1, -1),
+                    x[:, 1:],
+                ),
+                dim=1,
+            )
+
+        return x
+
+    def forward_features_list(self, x_list, masks_list, prompt_list):
+        x = [self.prepare_tokens_with_masks(x, masks, prompt) for x, masks, prompt in zip(x_list, masks_list, prompt_list)]
+        for blk in self.blocks:
+            x = blk(x)
+
+        all_x = x
+        output = []
+        for x, masks in zip(all_x, masks_list):
+            x_norm = self.norm(x)
+            output.append(
+                {
+                    "x_norm_clstoken": x_norm[:, 0],
+                    "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+                    "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+                    "x_prenorm": x,
+                    "masks": masks,
+                }
+            )
+        return output
+
+    def forward_features(self, x, masks=None, prompt=None):
+        # print("forward_features prompt: ", prompt)
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks, prompt)
+
+        x = self.prepare_tokens_with_masks(x, masks, prompt)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x_norm = self.norm(x)
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+            "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+            "x_prenorm": x,
+            "masks": masks,
+        }
+
+    def _get_intermediate_layers_not_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        # If n is an int, take the n last blocks. If it's a list, take them
+        output, total_block_len = [], len(self.blocks)
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in blocks_to_take:
+                output.append(x)
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def _get_intermediate_layers_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        output, i, total_block_len = [], 0, len(self.blocks[-1])
+        # If n is an int, take the n last blocks. If it's a list, take them
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for block_chunk in self.blocks:
+            for blk in block_chunk[i:]:  # Passing the nn.Identity()
+                x = blk(x)
+                if i in blocks_to_take:
+                    output.append(x)
+                i += 1
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,  # Layers or n last layers to take
+        reshape: bool = False,
+        return_class_token: bool = False,
+        norm=True,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        if self.chunked_blocks:
+            outputs = self._get_intermediate_layers_chunked(x, n)
+        else:
+            outputs = self._get_intermediate_layers_not_chunked(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        class_tokens = [out[:, 0] for out in outputs]
+        outputs = [out[:, 1 + self.num_register_tokens :] for out in outputs]
+        if reshape:
+            B, _, w, h = x.shape
+            outputs = [
+                out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+        if return_class_token:
+            return tuple(zip(outputs, class_tokens))
+        return tuple(outputs)
+
+    def forward(self, *args, is_training=False, **kwargs):
+        ret = self.forward_features(*args, **kwargs)
+        if is_training:
+            return ret
+        else:
+            return self.head(ret["x_norm_clstoken"])
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = ""):
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+
+def vit_small(patch_size=16, num_register_tokens=0, init_values=1.0, block_chunks=0,  **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        init_values=init_values,  # for layerscale: None or 0 => no layerscale
+        block_chunks=block_chunks,
+        **kwargs,
+    )
+    return model
+
+
+def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
+    """
+    Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
+    """
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1536,
+        depth=40,
+        num_heads=24,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
diff --git a/src/dinov2/run/__init__.py b/src/dinov2/run/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9
--- /dev/null
+++ b/src/dinov2/run/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/src/dinov2/run/eval/knn.py b/src/dinov2/run/eval/knn.py
new file mode 100644
index 0000000000000000000000000000000000000000..d11918445cdfe415fe58ac8b3ad0bf29702e3457
--- /dev/null
+++ b/src/dinov2/run/eval/knn.py
@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+
+from dinov2.eval.knn import get_args_parser as get_knn_args_parser
+from dinov2.logging import setup_logging
+from dinov2.run.submit import get_args_parser, submit_jobs
+
+
+logger = logging.getLogger("dinov2")
+
+
+class Evaluator:
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        from dinov2.eval.knn import main as knn_main
+
+        self._setup_args()
+        knn_main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        logger.info(f"Requeuing {self.args}")
+        empty = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty)
+
+    def _setup_args(self):
+        import submitit
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id))
+        logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+        logger.info(f"Args: {self.args}")
+
+
+def main():
+    description = "Submitit launcher for DINOv2 k-NN evaluation"
+    knn_args_parser = get_knn_args_parser(add_help=False)
+    parents = [knn_args_parser]
+    args_parser = get_args_parser(description=description, parents=parents)
+    args = args_parser.parse_args()
+
+    setup_logging()
+
+    assert os.path.exists(args.config_file), "Configuration file does not exist!"
+    submit_jobs(Evaluator, args, name="dinov2:knn")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/dinov2/run/eval/linear.py b/src/dinov2/run/eval/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1dc3293e88512a5cf885ab775dc08e01aed6724
--- /dev/null
+++ b/src/dinov2/run/eval/linear.py
@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+
+from dinov2.eval.linear import get_args_parser as get_linear_args_parser
+from dinov2.logging import setup_logging
+from dinov2.run.submit import get_args_parser, submit_jobs
+
+
+logger = logging.getLogger("dinov2")
+
+
+class Evaluator:
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        from dinov2.eval.linear import main as linear_main
+
+        self._setup_args()
+        linear_main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        logger.info(f"Requeuing {self.args}")
+        empty = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty)
+
+    def _setup_args(self):
+        import submitit
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id))
+        logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+        logger.info(f"Args: {self.args}")
+
+
+def main():
+    description = "Submitit launcher for DINOv2 linear evaluation"
+    linear_args_parser = get_linear_args_parser(add_help=False)
+    parents = [linear_args_parser]
+    args_parser = get_args_parser(description=description, parents=parents)
+    args = args_parser.parse_args()
+
+    setup_logging()
+
+    assert os.path.exists(args.config_file), "Configuration file does not exist!"
+    submit_jobs(Evaluator, args, name="dinov2:linear")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/dinov2/run/eval/log_regression.py b/src/dinov2/run/eval/log_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdf02181122de72cfa463ef38494967219df9cf3
--- /dev/null
+++ b/src/dinov2/run/eval/log_regression.py
@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+
+from dinov2.eval.log_regression import get_args_parser as get_log_regression_args_parser
+from dinov2.logging import setup_logging
+from dinov2.run.submit import get_args_parser, submit_jobs
+
+
+logger = logging.getLogger("dinov2")
+
+
+class Evaluator:
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        from dinov2.eval.log_regression import main as log_regression_main
+
+        self._setup_args()
+        log_regression_main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        logger.info(f"Requeuing {self.args}")
+        empty = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty)
+
+    def _setup_args(self):
+        import submitit
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id))
+        logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+        logger.info(f"Args: {self.args}")
+
+
+def main():
+    description = "Submitit launcher for DINOv2 logistic evaluation"
+    log_regression_args_parser = get_log_regression_args_parser(add_help=False)
+    parents = [log_regression_args_parser]
+    args_parser = get_args_parser(description=description, parents=parents)
+    args = args_parser.parse_args()
+
+    setup_logging()
+
+    assert os.path.exists(args.config_file), "Configuration file does not exist!"
+    submit_jobs(Evaluator, args, name="dinov2:logreg")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/dinov2/run/submit.py b/src/dinov2/run/submit.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d1f718e704cf9a48913422404c25a7fcc50e738
--- /dev/null
+++ b/src/dinov2/run/submit.py
@@ -0,0 +1,122 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import argparse
+import logging
+import os
+from pathlib import Path
+from typing import List, Optional
+
+import submitit
+
+from dinov2.utils.cluster import (
+    get_slurm_executor_parameters,
+    get_slurm_partition,
+    get_user_checkpoint_path,
+)
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+) -> argparse.ArgumentParser:
+    parents = parents or []
+    slurm_partition = get_slurm_partition()
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents,
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--ngpus",
+        "--gpus",
+        "--gpus-per-node",
+        default=8,
+        type=int,
+        help="Number of GPUs to request on each node",
+    )
+    parser.add_argument(
+        "--nodes",
+        "--nnodes",
+        default=1,
+        type=int,
+        help="Number of nodes to request",
+    )
+    parser.add_argument(
+        "--timeout",
+        default=2800,
+        type=int,
+        help="Duration of the job",
+    )
+    parser.add_argument(
+        "--partition",
+        default=slurm_partition,
+        type=str,
+        help="Partition where to submit",
+    )
+    parser.add_argument(
+        "--use-volta32",
+        action="store_true",
+        help="Request V100-32GB GPUs",
+    )
+    parser.add_argument(
+        "--comment",
+        default="",
+        type=str,
+        help="Comment to pass to scheduler, e.g. priority message",
+    )
+    parser.add_argument(
+        "--exclude",
+        default="",
+        type=str,
+        help="Nodes to exclude",
+    )
+    return parser
+
+
+def get_shared_folder() -> Path:
+    user_checkpoint_path = get_user_checkpoint_path()
+    if user_checkpoint_path is None:
+        raise RuntimeError("Path to user checkpoint cannot be determined")
+    path = user_checkpoint_path / "experiments"
+    path.mkdir(exist_ok=True)
+    return path
+
+
+def submit_jobs(task_class, args, name: str):
+    if not args.output_dir:
+        args.output_dir = str(get_shared_folder() / "%j")
+
+    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+    executor = submitit.AutoExecutor(folder=args.output_dir, slurm_max_num_timeout=30)
+
+    kwargs = {}
+    if args.use_volta32:
+        kwargs["slurm_constraint"] = "volta32gb"
+    if args.comment:
+        kwargs["slurm_comment"] = args.comment
+    if args.exclude:
+        kwargs["slurm_exclude"] = args.exclude
+
+    executor_params = get_slurm_executor_parameters(
+        nodes=args.nodes,
+        num_gpus_per_node=args.ngpus,
+        timeout_min=args.timeout,  # max is 60 * 72
+        slurm_signal_delay_s=120,
+        slurm_partition=args.partition,
+        **kwargs,
+    )
+    executor.update_parameters(name=name, **executor_params)
+
+    task = task_class(args)
+    job = executor.submit(task)
+
+    logger.info(f"Submitted job_id: {job.job_id}")
+    str_output_dir = os.path.abspath(args.output_dir).replace("%j", str(job.job_id))
+    logger.info(f"Logs and checkpoints will be saved at: {str_output_dir}")
diff --git a/src/dinov2/run/train/train.py b/src/dinov2/run/train/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2366e9bf79765e6abcd70dda6b43f31cb7093eb
--- /dev/null
+++ b/src/dinov2/run/train/train.py
@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+
+from dinov2.logging import setup_logging
+from dinov2.train import get_args_parser as get_train_args_parser
+from dinov2.run.submit import get_args_parser, submit_jobs
+
+
+logger = logging.getLogger("dinov2")
+
+
+class Trainer(object):
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        from dinov2.train import main as train_main
+
+        self._setup_args()
+        train_main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        logger.info(f"Requeuing {self.args}")
+        empty = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty)
+
+    def _setup_args(self):
+        import submitit
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id))
+        logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+        logger.info(f"Args: {self.args}")
+
+
+def main():
+    description = "Submitit launcher for DINOv2 training"
+    train_args_parser = get_train_args_parser(add_help=False)
+    parents = [train_args_parser]
+    args_parser = get_args_parser(description=description, parents=parents)
+    args = args_parser.parse_args()
+
+    setup_logging()
+
+    assert os.path.exists(args.config_file), "Configuration file does not exist!"
+    submit_jobs(Trainer, args, name="dinov2:train")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/dinov2/train/__init__.py b/src/dinov2/train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f1752922d04fff0112eb7796be28ff6b68c6073
--- /dev/null
+++ b/src/dinov2/train/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .train import get_args_parser, main
+from .ssl_meta_arch import SSLMetaArch
diff --git a/src/dinov2/train/ssl_meta_arch.py b/src/dinov2/train/ssl_meta_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ccf15e904ebeb6134dfb4f5c99da4fc8d41b8e4
--- /dev/null
+++ b/src/dinov2/train/ssl_meta_arch.py
@@ -0,0 +1,400 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from functools import partial
+import logging
+
+import torch
+from torch import nn
+
+from dinov2.loss import DINOLoss, iBOTPatchLoss, KoLeoLoss
+from dinov2.models import build_model_from_cfg
+from dinov2.layers import DINOHead
+from dinov2.utils.utils import has_batchnorms
+from dinov2.utils.param_groups import get_params_groups_with_decay, fuse_params_groups
+from dinov2.fsdp import get_fsdp_wrapper, ShardedGradScaler, get_fsdp_modules, reshard_fsdp_model
+
+from dinov2.models.vision_transformer import BlockChunk
+
+
+try:
+    from xformers.ops import fmha
+except ImportError:
+    raise AssertionError("xFormers is required for training")
+
+
+logger = logging.getLogger("dinov2")
+
+
+class SSLMetaArch(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+        self.fp16_scaler = ShardedGradScaler() if cfg.compute_precision.grad_scaler else None
+
+        student_model_dict = dict()
+        teacher_model_dict = dict()
+
+        student_backbone, teacher_backbone, embed_dim = build_model_from_cfg(cfg)
+        student_model_dict["backbone"] = student_backbone
+        teacher_model_dict["backbone"] = teacher_backbone
+        logger.info(f"OPTIONS -- architecture : embed_dim: {embed_dim}")
+
+        if cfg.student.pretrained_weights:
+            chkpt = torch.load(cfg.student.pretrained_weights)
+            logger.info(f"OPTIONS -- pretrained weights: loading from {cfg.student.pretrained_weights}")
+            student_backbone.load_state_dict(chkpt["model"], strict=False)
+
+        self.embed_dim = embed_dim
+        self.dino_out_dim = cfg.dino.head_n_prototypes
+
+        self.do_dino = cfg.dino.loss_weight > 0
+        self.do_koleo = cfg.dino.koleo_loss_weight > 0
+        self.do_ibot = cfg.ibot.loss_weight > 0
+        self.ibot_separate_head = cfg.ibot.separate_head
+
+        logger.info("OPTIONS -- DINO")
+        if self.do_dino:
+            logger.info(f"OPTIONS -- DINO -- loss_weight: {cfg.dino.loss_weight}")
+            logger.info(f"OPTIONS -- DINO -- head_n_prototypes: {cfg.dino.head_n_prototypes}")
+            logger.info(f"OPTIONS -- DINO -- head_bottleneck_dim: {cfg.dino.head_bottleneck_dim}")
+            logger.info(f"OPTIONS -- DINO -- head_hidden_dim: {cfg.dino.head_hidden_dim}")
+            self.dino_loss_weight = cfg.dino.loss_weight
+            dino_head = partial(
+                DINOHead,
+                in_dim=embed_dim,
+                out_dim=cfg.dino.head_n_prototypes,
+                hidden_dim=cfg.dino.head_hidden_dim,
+                bottleneck_dim=cfg.dino.head_bottleneck_dim,
+                nlayers=cfg.dino.head_nlayers,
+            )
+            self.dino_loss = DINOLoss(self.dino_out_dim)
+            if self.do_koleo:
+                logger.info("OPTIONS -- DINO -- applying KOLEO regularization")
+                self.koleo_loss = KoLeoLoss()
+
+        else:
+            logger.info("OPTIONS -- DINO -- not using DINO")
+
+        if self.do_dino or self.do_ibot:
+            student_model_dict["dino_head"] = dino_head()
+            teacher_model_dict["dino_head"] = dino_head()
+
+        logger.info("OPTIONS -- IBOT")
+        logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}")
+        logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_ratio_tuple: {cfg.ibot.mask_ratio_min_max}")
+        logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_sample_probability: {cfg.ibot.mask_sample_probability}")
+        if self.do_ibot:
+            self.ibot_loss_weight = cfg.ibot.loss_weight
+            assert max(cfg.ibot.mask_ratio_min_max) > 0, "please provide a positive mask ratio tuple for ibot"
+            assert cfg.ibot.mask_sample_probability > 0, "please provide a positive mask probability for ibot"
+            self.ibot_out_dim = cfg.ibot.head_n_prototypes if self.ibot_separate_head else cfg.dino.head_n_prototypes
+            self.ibot_patch_loss = iBOTPatchLoss(self.ibot_out_dim)
+            if self.ibot_separate_head:
+                logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}")
+                logger.info(f"OPTIONS -- IBOT -- head_n_prototypes: {cfg.ibot.head_n_prototypes}")
+                logger.info(f"OPTIONS -- IBOT -- head_bottleneck_dim: {cfg.ibot.head_bottleneck_dim}")
+                logger.info(f"OPTIONS -- IBOT -- head_hidden_dim: {cfg.ibot.head_hidden_dim}")
+                ibot_head = partial(
+                    DINOHead,
+                    in_dim=embed_dim,
+                    out_dim=cfg.ibot.head_n_prototypes,
+                    hidden_dim=cfg.ibot.head_hidden_dim,
+                    bottleneck_dim=cfg.ibot.head_bottleneck_dim,
+                    nlayers=cfg.ibot.head_nlayers,
+                )
+                student_model_dict["ibot_head"] = ibot_head()
+                teacher_model_dict["ibot_head"] = ibot_head()
+            else:
+                logger.info("OPTIONS -- IBOT -- head shared with DINO")
+
+        self.need_to_synchronize_fsdp_streams = True
+
+        self.student = nn.ModuleDict(student_model_dict)
+        self.teacher = nn.ModuleDict(teacher_model_dict)
+
+        # there is no backpropagation through the teacher, so no need for gradients
+        for p in self.teacher.parameters():
+            p.requires_grad = False
+        logger.info(f"Student and Teacher are built: they are both {cfg.student.arch} network.")
+
+    def forward(self, inputs):
+        raise NotImplementedError
+
+    def backprop_loss(self, loss):
+        if self.fp16_scaler is not None:
+            self.fp16_scaler.scale(loss).backward()
+        else:
+            loss.backward()
+
+    def forward_backward(self, images, teacher_temp):
+        n_global_crops = 2
+        assert n_global_crops == 2
+        n_local_crops = self.cfg.crops.local_crops_number
+
+        global_crops = images["collated_global_crops"].cuda(non_blocking=True)
+        local_crops = images["collated_local_crops"].cuda(non_blocking=True)
+
+        masks = images["collated_masks"].cuda(non_blocking=True)
+        mask_indices_list = images["mask_indices_list"].cuda(non_blocking=True)
+        n_masked_patches_tensor = images["n_masked_patches"].cuda(non_blocking=True)
+        n_masked_patches = mask_indices_list.shape[0]
+        upperbound = images["upperbound"]
+        masks_weight = images["masks_weight"].cuda(non_blocking=True)
+
+        n_local_crops_loss_terms = max(n_local_crops * n_global_crops, 1)
+        n_global_crops_loss_terms = (n_global_crops - 1) * n_global_crops
+
+        do_dino = self.do_dino
+        do_ibot = self.do_ibot
+
+        # loss scales
+        ibot_loss_scale = 1.0 / n_global_crops
+
+        # teacher output
+        @torch.no_grad()
+        def get_teacher_output():
+            x, n_global_crops_teacher = global_crops, n_global_crops
+            teacher_backbone_output_dict = self.teacher.backbone(x, is_training=True)
+            teacher_cls_tokens = teacher_backbone_output_dict["x_norm_clstoken"]
+            teacher_cls_tokens = teacher_cls_tokens.chunk(n_global_crops_teacher)
+            # watch out: these are chunked and cat'd in reverse so A is matched to B in the global crops dino loss
+            teacher_cls_tokens = torch.cat((teacher_cls_tokens[1], teacher_cls_tokens[0]))
+            ibot_teacher_patch_tokens = teacher_backbone_output_dict["x_norm_patchtokens"]
+            _dim = ibot_teacher_patch_tokens.shape[-1]
+            n_cls_tokens = teacher_cls_tokens.shape[0]
+
+            if do_ibot and not self.ibot_separate_head:
+                buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound + n_cls_tokens, _dim)
+                buffer_tensor_teacher[:n_cls_tokens].copy_(teacher_cls_tokens)
+                torch.index_select(
+                    ibot_teacher_patch_tokens.flatten(0, 1),
+                    dim=0,
+                    index=mask_indices_list,
+                    out=buffer_tensor_teacher[n_cls_tokens : n_cls_tokens + n_masked_patches],
+                )
+                tokens_after_head = self.teacher.dino_head(buffer_tensor_teacher)
+                teacher_cls_tokens_after_head = tokens_after_head[:n_cls_tokens]
+                masked_teacher_patch_tokens_after_head = tokens_after_head[
+                    n_cls_tokens : n_cls_tokens + n_masked_patches
+                ]
+            elif do_ibot and self.ibot_separate_head:
+                buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound, _dim)
+                torch.index_select(
+                    ibot_teacher_patch_tokens.flatten(0, 1),
+                    dim=0,
+                    index=mask_indices_list,
+                    out=buffer_tensor_teacher[:n_masked_patches],
+                )
+                teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens)
+                masked_teacher_patch_tokens_after_head = self.teacher.ibot_head(buffer_tensor_teacher)[
+                    :n_masked_patches
+                ]
+            else:
+                teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens)
+                masked_teacher_ibot_softmaxed_centered = None
+
+            if self.cfg.train.centering == "centering":
+                teacher_dino_softmaxed_centered_list = self.dino_loss.softmax_center_teacher(
+                    teacher_cls_tokens_after_head, teacher_temp=teacher_temp
+                ).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:])
+                self.dino_loss.update_center(teacher_cls_tokens_after_head)
+                if do_ibot:
+                    masked_teacher_patch_tokens_after_head = masked_teacher_patch_tokens_after_head.unsqueeze(0)
+                    masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.softmax_center_teacher(
+                        masked_teacher_patch_tokens_after_head[:, :n_masked_patches], teacher_temp=teacher_temp
+                    )
+                    masked_teacher_ibot_softmaxed_centered = masked_teacher_ibot_softmaxed_centered.squeeze(0)
+                    self.ibot_patch_loss.update_center(masked_teacher_patch_tokens_after_head[:n_masked_patches])
+
+            elif self.cfg.train.centering == "sinkhorn_knopp":
+                teacher_dino_softmaxed_centered_list = self.dino_loss.sinkhorn_knopp_teacher(
+                    teacher_cls_tokens_after_head, teacher_temp=teacher_temp
+                ).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:])
+
+                if do_ibot:
+                    masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.sinkhorn_knopp_teacher(
+                        masked_teacher_patch_tokens_after_head,
+                        teacher_temp=teacher_temp,
+                        n_masked_patches_tensor=n_masked_patches_tensor,
+                    )
+
+            else:
+                raise NotImplementedError
+
+            return teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered
+
+        teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered = get_teacher_output()
+        reshard_fsdp_model(self.teacher)
+
+        loss_dict = {}
+
+        loss_accumulator = 0  # for backprop
+        student_global_backbone_output_dict, student_local_backbone_output_dict = self.student.backbone(
+            [global_crops, local_crops], masks=[masks, None], is_training=True
+        )
+
+        inputs_for_student_head_list = []
+
+        # 1a: local crops cls tokens
+        student_local_cls_tokens = student_local_backbone_output_dict["x_norm_clstoken"]
+        inputs_for_student_head_list.append(student_local_cls_tokens.unsqueeze(0))
+
+        # 1b: global crops cls tokens
+        student_global_cls_tokens = student_global_backbone_output_dict["x_norm_clstoken"]
+        inputs_for_student_head_list.append(student_global_cls_tokens.unsqueeze(0))
+
+        # 1c: global crops patch tokens
+        if do_ibot:
+            _dim = student_global_backbone_output_dict["x_norm_clstoken"].shape[-1]
+            ibot_student_patch_tokens = student_global_backbone_output_dict["x_norm_patchtokens"]
+            buffer_tensor_patch_tokens = ibot_student_patch_tokens.new_zeros(upperbound, _dim)
+            buffer_tensor_patch_tokens[:n_masked_patches].copy_(
+                torch.index_select(ibot_student_patch_tokens.flatten(0, 1), dim=0, index=mask_indices_list)
+            )
+            if not self.ibot_separate_head:
+                inputs_for_student_head_list.append(buffer_tensor_patch_tokens.unsqueeze(0))
+            else:
+                student_global_masked_patch_tokens_after_head = self.student.ibot_head(buffer_tensor_patch_tokens)[
+                    :n_masked_patches
+                ]
+
+        # 2: run
+        _attn_bias, cat_inputs = fmha.BlockDiagonalMask.from_tensor_list(inputs_for_student_head_list)
+        outputs_list = _attn_bias.split(self.student.dino_head(cat_inputs))
+
+        # 3a: local crops cls tokens
+        student_local_cls_tokens_after_head = outputs_list.pop(0).squeeze(0)
+
+        # 3b: global crops cls tokens
+        student_global_cls_tokens_after_head = outputs_list.pop(0).squeeze(0)
+
+        # 3c: global crops patch tokens
+        if do_ibot and not self.ibot_separate_head:
+            student_global_masked_patch_tokens_after_head = outputs_list.pop(0).squeeze(0)[:n_masked_patches]
+
+        if n_local_crops > 0:
+            dino_local_crops_loss = self.dino_loss(
+                student_output_list=student_local_cls_tokens_after_head.chunk(n_local_crops),
+                teacher_out_softmaxed_centered_list=teacher_dino_softmaxed_centered_list,
+            ) / (n_global_crops_loss_terms + n_local_crops_loss_terms)
+
+            # store for display
+            loss_dict["dino_local_crops_loss"] = dino_local_crops_loss
+
+            # accumulate loss
+            loss_accumulator += self.dino_loss_weight * dino_local_crops_loss
+
+        # process global crops
+        loss_scales = 2  # this is here since we process global crops together
+
+        if do_dino:
+            # compute loss
+            dino_global_crops_loss = (
+                self.dino_loss(
+                    student_output_list=[student_global_cls_tokens_after_head],
+                    teacher_out_softmaxed_centered_list=[
+                        teacher_dino_softmaxed_centered_list.flatten(0, 1)
+                    ],  # these were chunked and stacked in reverse so A is matched to B
+                )
+                * loss_scales
+                / (n_global_crops_loss_terms + n_local_crops_loss_terms)
+            )
+
+            loss_dict["dino_global_crops_loss"] = dino_global_crops_loss
+
+            # accumulate loss
+            loss_accumulator += self.dino_loss_weight * dino_global_crops_loss
+
+            student_cls_tokens = student_global_cls_tokens
+
+            if self.do_koleo:
+                koleo_loss = self.cfg.dino.koleo_loss_weight * sum(
+                    self.koleo_loss(p) for p in student_cls_tokens.chunk(2)
+                )  # we don't apply koleo loss between cls tokens of a same image
+                loss_accumulator += koleo_loss
+                loss_dict["koleo_loss"] = (
+                    koleo_loss / loss_scales
+                )  # this is to display the same losses as before but we can remove eventually
+
+        if do_ibot:
+            # compute loss
+            ibot_patch_loss = (
+                self.ibot_patch_loss.forward_masked(
+                    student_global_masked_patch_tokens_after_head,
+                    masked_teacher_ibot_softmaxed_centered,
+                    student_masks_flat=masks,
+                    n_masked_patches=n_masked_patches,
+                    masks_weight=masks_weight,
+                )
+                * loss_scales
+                * ibot_loss_scale
+            )
+
+            # store for display
+            loss_dict["ibot_loss"] = ibot_patch_loss / 2
+
+            # accumulate loss
+            loss_accumulator += self.ibot_loss_weight * ibot_patch_loss
+
+        self.backprop_loss(loss_accumulator)
+
+        self.fsdp_synchronize_streams()
+
+        return loss_dict
+
+    def fsdp_synchronize_streams(self):
+        if self.need_to_synchronize_fsdp_streams:
+            torch.cuda.synchronize()
+            self.student.dino_head._streams = (
+                self.teacher.dino_head._streams
+            ) = self.student.backbone._streams = self.teacher.backbone._streams
+            self.need_to_synchronize_fsdp_streams = False
+
+    def update_teacher(self, m):
+        student_param_list = []
+        teacher_param_list = []
+        with torch.no_grad():
+            for k in self.student.keys():
+                for ms, mt in zip(get_fsdp_modules(self.student[k]), get_fsdp_modules(self.teacher[k])):
+                    student_param_list += ms.params
+                    teacher_param_list += mt.params
+            torch._foreach_mul_(teacher_param_list, m)
+            torch._foreach_add_(teacher_param_list, student_param_list, alpha=1 - m)
+
+    def train(self):
+        super().train()
+        self.teacher.eval()
+
+    def get_maybe_fused_params_for_submodel(self, m):
+        params_groups = get_params_groups_with_decay(
+            model=m,
+            lr_decay_rate=self.cfg.optim.layerwise_decay,
+            patch_embed_lr_mult=self.cfg.optim.patch_embed_lr_mult,
+        )
+        fused_params_groups = fuse_params_groups(params_groups)
+        logger.info("fusing param groups")
+
+        for g in fused_params_groups:
+            g["foreach"] = True
+        return fused_params_groups
+
+    def get_params_groups(self):
+        all_params_groups = []
+        for m in self.student.values():
+            all_params_groups += self.get_maybe_fused_params_for_submodel(m)
+        return all_params_groups
+
+    def prepare_for_distributed_training(self):
+        logger.info("DISTRIBUTED FSDP -- preparing model for distributed training")
+        if has_batchnorms(self.student):
+            raise NotImplementedError
+        # below will synchronize all student subnetworks across gpus:
+        for k, v in self.student.items():
+            self.teacher[k].load_state_dict(self.student[k].state_dict())
+            student_model_cfg = self.cfg.compute_precision.student[k]
+            self.student[k] = get_fsdp_wrapper(student_model_cfg, modules_to_wrap={BlockChunk})(self.student[k])
+            teacher_model_cfg = self.cfg.compute_precision.teacher[k]
+            self.teacher[k] = get_fsdp_wrapper(teacher_model_cfg, modules_to_wrap={BlockChunk})(self.teacher[k])
diff --git a/src/dinov2/train/train.py b/src/dinov2/train/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..473b8d01473654182de9f91c94a2d8720fe096a5
--- /dev/null
+++ b/src/dinov2/train/train.py
@@ -0,0 +1,318 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import argparse
+import logging
+import math
+import os
+from functools import partial
+
+from fvcore.common.checkpoint import PeriodicCheckpointer
+import torch
+
+from dinov2.data import SamplerType, make_data_loader, make_dataset
+from dinov2.data import collate_data_and_cast, DataAugmentationDINO, MaskingGenerator
+import dinov2.distributed as distributed
+from dinov2.fsdp import FSDPCheckpointer
+from dinov2.logging import MetricLogger
+from dinov2.utils.config import setup
+from dinov2.utils.utils import CosineScheduler
+
+from dinov2.train.ssl_meta_arch import SSLMetaArch
+
+
+torch.backends.cuda.matmul.allow_tf32 = True  # PyTorch 1.12 sets this to False by default
+logger = logging.getLogger("dinov2")
+
+
+def get_args_parser(add_help: bool = True):
+    parser = argparse.ArgumentParser("DINOv2 training", add_help=add_help)
+    parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
+    parser.add_argument(
+        "--no-resume",
+        action="store_true",
+        help="Whether to not attempt to resume from the checkpoint directory. ",
+    )
+    parser.add_argument("--eval-only", action="store_true", help="perform evaluation only")
+    parser.add_argument("--eval", type=str, default="", help="Eval type to perform")
+    parser.add_argument(
+        "opts",
+        help="""
+Modify config options at the end of the command. For Yacs configs, use
+space-separated "PATH.KEY VALUE" pairs.
+For python-based LazyConfig, use "path.key=value".
+        """.strip(),
+        default=None,
+        nargs=argparse.REMAINDER,
+    )
+    parser.add_argument(
+        "--output-dir",
+        "--output_dir",
+        default="",
+        type=str,
+        help="Output directory to save logs and checkpoints",
+    )
+
+    return parser
+
+
+def build_optimizer(cfg, params_groups):
+    return torch.optim.AdamW(params_groups, betas=(cfg.optim.adamw_beta1, cfg.optim.adamw_beta2))
+
+
+def build_schedulers(cfg):
+    OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH
+    lr = dict(
+        base_value=cfg.optim["lr"],
+        final_value=cfg.optim["min_lr"],
+        total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH,
+        warmup_iters=cfg.optim["warmup_epochs"] * OFFICIAL_EPOCH_LENGTH,
+        start_warmup_value=0,
+    )
+    wd = dict(
+        base_value=cfg.optim["weight_decay"],
+        final_value=cfg.optim["weight_decay_end"],
+        total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH,
+    )
+    momentum = dict(
+        base_value=cfg.teacher["momentum_teacher"],
+        final_value=cfg.teacher["final_momentum_teacher"],
+        total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH,
+    )
+    teacher_temp = dict(
+        base_value=cfg.teacher["teacher_temp"],
+        final_value=cfg.teacher["teacher_temp"],
+        total_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH,
+        warmup_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH,
+        start_warmup_value=cfg.teacher["warmup_teacher_temp"],
+    )
+
+    lr_schedule = CosineScheduler(**lr)
+    wd_schedule = CosineScheduler(**wd)
+    momentum_schedule = CosineScheduler(**momentum)
+    teacher_temp_schedule = CosineScheduler(**teacher_temp)
+    last_layer_lr_schedule = CosineScheduler(**lr)
+
+    last_layer_lr_schedule.schedule[
+        : cfg.optim["freeze_last_layer_epochs"] * OFFICIAL_EPOCH_LENGTH
+    ] = 0  # mimicking the original schedules
+
+    logger.info("Schedulers ready.")
+
+    return (
+        lr_schedule,
+        wd_schedule,
+        momentum_schedule,
+        teacher_temp_schedule,
+        last_layer_lr_schedule,
+    )
+
+
+def apply_optim_scheduler(optimizer, lr, wd, last_layer_lr):
+    for param_group in optimizer.param_groups:
+        is_last_layer = param_group["is_last_layer"]
+        lr_multiplier = param_group["lr_multiplier"]
+        wd_multiplier = param_group["wd_multiplier"]
+        param_group["weight_decay"] = wd * wd_multiplier
+        param_group["lr"] = (last_layer_lr if is_last_layer else lr) * lr_multiplier
+
+
+def do_test(cfg, model, iteration):
+    new_state_dict = model.teacher.state_dict()
+
+    if distributed.is_main_process():
+        iterstring = str(iteration)
+        eval_dir = os.path.join(cfg.train.output_dir, "eval", iterstring)
+        os.makedirs(eval_dir, exist_ok=True)
+        # save teacher checkpoint
+        teacher_ckp_path = os.path.join(eval_dir, "teacher_checkpoint.pth")
+        torch.save({"teacher": new_state_dict}, teacher_ckp_path)
+
+
+def do_train(cfg, model, resume=False):
+    model.train()
+    inputs_dtype = torch.half
+    fp16_scaler = model.fp16_scaler  # for mixed precision training
+
+    # setup optimizer
+
+    optimizer = build_optimizer(cfg, model.get_params_groups())
+    (
+        lr_schedule,
+        wd_schedule,
+        momentum_schedule,
+        teacher_temp_schedule,
+        last_layer_lr_schedule,
+    ) = build_schedulers(cfg)
+
+    # checkpointer
+    checkpointer = FSDPCheckpointer(model, cfg.train.output_dir, optimizer=optimizer, save_to_disk=True)
+
+    start_iter = checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1
+
+    OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH
+    max_iter = cfg.optim.epochs * OFFICIAL_EPOCH_LENGTH
+
+    periodic_checkpointer = PeriodicCheckpointer(
+        checkpointer,
+        period=3 * OFFICIAL_EPOCH_LENGTH,
+        max_iter=max_iter,
+        max_to_keep=3,
+    )
+
+    # setup data preprocessing
+
+    img_size = cfg.crops.global_crops_size
+    patch_size = cfg.student.patch_size
+    n_tokens = (img_size // patch_size) ** 2
+    mask_generator = MaskingGenerator(
+        input_size=(img_size // patch_size, img_size // patch_size),
+        max_num_patches=0.5 * img_size // patch_size * img_size // patch_size,
+    )
+
+    data_transform = DataAugmentationDINO(
+        cfg.crops.global_crops_scale,
+        cfg.crops.local_crops_scale,
+        cfg.crops.local_crops_number,
+        global_crops_size=cfg.crops.global_crops_size,
+        local_crops_size=cfg.crops.local_crops_size,
+    )
+
+    collate_fn = partial(
+        collate_data_and_cast,
+        mask_ratio_tuple=cfg.ibot.mask_ratio_min_max,
+        mask_probability=cfg.ibot.mask_sample_probability,
+        n_tokens=n_tokens,
+        mask_generator=mask_generator,
+        dtype=inputs_dtype,
+    )
+
+    # setup data loader
+
+    dataset = make_dataset(
+        dataset_str=cfg.train.dataset_path,
+        transform=data_transform,
+        target_transform=lambda _: (),
+    )
+    # sampler_type = SamplerType.INFINITE
+    sampler_type = SamplerType.SHARDED_INFINITE
+    data_loader = make_data_loader(
+        dataset=dataset,
+        batch_size=cfg.train.batch_size_per_gpu,
+        num_workers=cfg.train.num_workers,
+        shuffle=True,
+        seed=start_iter,  # TODO: Fix this -- cfg.train.seed
+        sampler_type=sampler_type,
+        sampler_advance=0,  # TODO(qas): fix this -- start_iter * cfg.train.batch_size_per_gpu,
+        drop_last=True,
+        collate_fn=collate_fn,
+    )
+
+    # training loop
+
+    iteration = start_iter
+
+    logger.info("Starting training from iteration {}".format(start_iter))
+    metrics_file = os.path.join(cfg.train.output_dir, "training_metrics.json")
+    metric_logger = MetricLogger(delimiter="  ", output_file=metrics_file)
+    header = "Training"
+
+    for data in metric_logger.log_every(
+        data_loader,
+        10,
+        header,
+        max_iter,
+        start_iter,
+    ):
+        current_batch_size = data["collated_global_crops"].shape[0] / 2
+        if iteration > max_iter:
+            return
+
+        # apply schedules
+
+        lr = lr_schedule[iteration]
+        wd = wd_schedule[iteration]
+        mom = momentum_schedule[iteration]
+        teacher_temp = teacher_temp_schedule[iteration]
+        last_layer_lr = last_layer_lr_schedule[iteration]
+        apply_optim_scheduler(optimizer, lr, wd, last_layer_lr)
+
+        # compute losses
+
+        optimizer.zero_grad(set_to_none=True)
+        loss_dict = model.forward_backward(data, teacher_temp=teacher_temp)
+
+        # clip gradients
+
+        if fp16_scaler is not None:
+            if cfg.optim.clip_grad:
+                fp16_scaler.unscale_(optimizer)
+                for v in model.student.values():
+                    v.clip_grad_norm_(cfg.optim.clip_grad)
+            fp16_scaler.step(optimizer)
+            fp16_scaler.update()
+        else:
+            if cfg.optim.clip_grad:
+                for v in model.student.values():
+                    v.clip_grad_norm_(cfg.optim.clip_grad)
+            optimizer.step()
+
+        # perform teacher EMA update
+
+        model.update_teacher(mom)
+
+        # logging
+
+        if distributed.get_global_size() > 1:
+            for v in loss_dict.values():
+                torch.distributed.all_reduce(v)
+        loss_dict_reduced = {k: v.item() / distributed.get_global_size() for k, v in loss_dict.items()}
+
+        if math.isnan(sum(loss_dict_reduced.values())):
+            logger.info("NaN detected")
+            raise AssertionError
+        losses_reduced = sum(loss for loss in loss_dict_reduced.values())
+
+        metric_logger.update(lr=lr)
+        metric_logger.update(wd=wd)
+        metric_logger.update(mom=mom)
+        metric_logger.update(last_layer_lr=last_layer_lr)
+        metric_logger.update(current_batch_size=current_batch_size)
+        metric_logger.update(total_loss=losses_reduced, **loss_dict_reduced)
+
+        # checkpointing and testing
+
+        if cfg.evaluation.eval_period_iterations > 0 and (iteration + 1) % cfg.evaluation.eval_period_iterations == 0:
+            do_test(cfg, model, f"training_{iteration}")
+            torch.cuda.synchronize()
+        periodic_checkpointer.step(iteration)
+
+        iteration = iteration + 1
+    metric_logger.synchronize_between_processes()
+    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
+
+
+def main(args):
+    cfg = setup(args)
+
+    model = SSLMetaArch(cfg).to(torch.device("cuda"))
+    model.prepare_for_distributed_training()
+
+    logger.info("Model:\n{}".format(model))
+    if args.eval_only:
+        iteration = (
+            FSDPCheckpointer(model, save_dir=cfg.train.output_dir)
+            .resume_or_load(cfg.MODEL.WEIGHTS, resume=not args.no_resume)
+            .get("iteration", -1)
+            + 1
+        )
+        return do_test(cfg, model, f"manual_{iteration}")
+
+    do_train(cfg, model, resume=not args.no_resume)
+
+
+if __name__ == "__main__":
+    args = get_args_parser(add_help=True).parse_args()
+    main(args)
diff --git a/src/dinov2/utils/__init__.py b/src/dinov2/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88da6bf80be92af00b72dfdb0a806fa64a7a2d9
--- /dev/null
+++ b/src/dinov2/utils/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/src/dinov2/utils/cluster.py b/src/dinov2/utils/cluster.py
new file mode 100644
index 0000000000000000000000000000000000000000..3df87dc3e1eb4f0f8a280dc3137cfef031886314
--- /dev/null
+++ b/src/dinov2/utils/cluster.py
@@ -0,0 +1,95 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+import os
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+
+class ClusterType(Enum):
+    AWS = "aws"
+    FAIR = "fair"
+    RSC = "rsc"
+
+
+def _guess_cluster_type() -> ClusterType:
+    uname = os.uname()
+    if uname.sysname == "Linux":
+        if uname.release.endswith("-aws"):
+            # Linux kernel versions on AWS instances are of the form "5.4.0-1051-aws"
+            return ClusterType.AWS
+        elif uname.nodename.startswith("rsc"):
+            # Linux kernel versions on RSC instances are standard ones but hostnames start with "rsc"
+            return ClusterType.RSC
+
+    return ClusterType.FAIR
+
+
+def get_cluster_type(cluster_type: Optional[ClusterType] = None) -> Optional[ClusterType]:
+    if cluster_type is None:
+        return _guess_cluster_type()
+
+    return cluster_type
+
+
+def get_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]:
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type is None:
+        return None
+
+    CHECKPOINT_DIRNAMES = {
+        ClusterType.AWS: "checkpoints",
+        ClusterType.FAIR: "checkpoint",
+        ClusterType.RSC: "checkpoint/dino",
+    }
+    return Path("/") / CHECKPOINT_DIRNAMES[cluster_type]
+
+
+def get_user_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]:
+    checkpoint_path = get_checkpoint_path(cluster_type)
+    if checkpoint_path is None:
+        return None
+
+    username = os.environ.get("USER")
+    assert username is not None
+    return checkpoint_path / username
+
+
+def get_slurm_partition(cluster_type: Optional[ClusterType] = None) -> Optional[str]:
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type is None:
+        return None
+
+    SLURM_PARTITIONS = {
+        ClusterType.AWS: "learnlab",
+        ClusterType.FAIR: "learnlab",
+        ClusterType.RSC: "learn",
+    }
+    return SLURM_PARTITIONS[cluster_type]
+
+
+def get_slurm_executor_parameters(
+    nodes: int, num_gpus_per_node: int, cluster_type: Optional[ClusterType] = None, **kwargs
+) -> Dict[str, Any]:
+    # create default parameters
+    params = {
+        "mem_gb": 0,  # Requests all memory on a node, see https://slurm.schedmd.com/sbatch.html
+        "gpus_per_node": num_gpus_per_node,
+        "tasks_per_node": num_gpus_per_node,  # one task per GPU
+        "cpus_per_task": 10,
+        "nodes": nodes,
+        "slurm_partition": get_slurm_partition(cluster_type),
+    }
+    # apply cluster-specific adjustments
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type == ClusterType.AWS:
+        params["cpus_per_task"] = 12
+        del params["mem_gb"]
+    elif cluster_type == ClusterType.RSC:
+        params["cpus_per_task"] = 12
+    # set additional parameters / apply overrides
+    params.update(kwargs)
+    return params
diff --git a/src/dinov2/utils/config.py b/src/dinov2/utils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9de578787bbcb376f8bd5a782206d0eb7ec1f52
--- /dev/null
+++ b/src/dinov2/utils/config.py
@@ -0,0 +1,72 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import math
+import logging
+import os
+
+from omegaconf import OmegaConf
+
+import dinov2.distributed as distributed
+from dinov2.logging import setup_logging
+from dinov2.utils import utils
+from dinov2.configs import dinov2_default_config
+
+
+logger = logging.getLogger("dinov2")
+
+
+def apply_scaling_rules_to_cfg(cfg):  # to fix
+    if cfg.optim.scaling_rule == "sqrt_wrt_1024":
+        base_lr = cfg.optim.base_lr
+        cfg.optim.lr = base_lr
+        cfg.optim.lr *= math.sqrt(cfg.train.batch_size_per_gpu * distributed.get_global_size() / 1024.0)
+        logger.info(f"sqrt scaling learning rate; base: {base_lr}, new: {cfg.optim.lr}")
+    else:
+        raise NotImplementedError
+    return cfg
+
+
+def write_config(cfg, output_dir, name="config.yaml"):
+    logger.info(OmegaConf.to_yaml(cfg))
+    saved_cfg_path = os.path.join(output_dir, name)
+    with open(saved_cfg_path, "w") as f:
+        OmegaConf.save(config=cfg, f=f)
+    return saved_cfg_path
+
+
+def get_cfg_from_args(args):
+    args.output_dir = os.path.abspath(args.output_dir)
+    args.opts += [f"train.output_dir={args.output_dir}"]
+    default_cfg = OmegaConf.create(dinov2_default_config)
+    cfg = OmegaConf.load(args.config_file)
+    cfg = OmegaConf.merge(default_cfg, cfg, OmegaConf.from_cli(args.opts))
+    return cfg
+
+
+def default_setup(args):
+    distributed.enable(overwrite=True)
+    seed = getattr(args, "seed", 0)
+    rank = distributed.get_global_rank()
+
+    global logger
+    setup_logging(output=args.output_dir, level=logging.INFO)
+    logger = logging.getLogger("dinov2")
+
+    utils.fix_random_seeds(seed + rank)
+    logger.info("git:\n  {}\n".format(utils.get_sha()))
+    logger.info("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items())))
+
+
+def setup(args):
+    """
+    Create configs and perform basic setups.
+    """
+    cfg = get_cfg_from_args(args)
+    os.makedirs(args.output_dir, exist_ok=True)
+    default_setup(args)
+    apply_scaling_rules_to_cfg(cfg)
+    write_config(cfg, args.output_dir)
+    return cfg
diff --git a/src/dinov2/utils/dtype.py b/src/dinov2/utils/dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..80f4cd74d99faa2731dbe9f8d3a13d71b3f8e3a8
--- /dev/null
+++ b/src/dinov2/utils/dtype.py
@@ -0,0 +1,37 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+
+from typing import Dict, Union
+
+import numpy as np
+import torch
+
+
+TypeSpec = Union[str, np.dtype, torch.dtype]
+
+
+_NUMPY_TO_TORCH_DTYPE: Dict[np.dtype, torch.dtype] = {
+    np.dtype("bool"): torch.bool,
+    np.dtype("uint8"): torch.uint8,
+    np.dtype("int8"): torch.int8,
+    np.dtype("int16"): torch.int16,
+    np.dtype("int32"): torch.int32,
+    np.dtype("int64"): torch.int64,
+    np.dtype("float16"): torch.float16,
+    np.dtype("float32"): torch.float32,
+    np.dtype("float64"): torch.float64,
+    np.dtype("complex64"): torch.complex64,
+    np.dtype("complex128"): torch.complex128,
+}
+
+
+def as_torch_dtype(dtype: TypeSpec) -> torch.dtype:
+    if isinstance(dtype, torch.dtype):
+        return dtype
+    if isinstance(dtype, str):
+        dtype = np.dtype(dtype)
+    assert isinstance(dtype, np.dtype), f"Expected an instance of nunpy dtype, got {type(dtype)}"
+    return _NUMPY_TO_TORCH_DTYPE[dtype]
diff --git a/src/dinov2/utils/param_groups.py b/src/dinov2/utils/param_groups.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a5d2ff627cddadc222e5f836864ee39c865208f
--- /dev/null
+++ b/src/dinov2/utils/param_groups.py
@@ -0,0 +1,103 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from collections import defaultdict
+import logging
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12, force_is_backbone=False, chunked_blocks=False):
+    """
+    Calculate lr decay rate for different ViT blocks.
+    Args:
+        name (string): parameter name.
+        lr_decay_rate (float): base lr decay rate.
+        num_layers (int): number of ViT blocks.
+    Returns:
+        lr decay rate for the given parameter.
+    """
+    layer_id = num_layers + 1
+    if name.startswith("backbone") or force_is_backbone:
+        if (
+            ".pos_embed" in name
+            or ".patch_embed" in name
+            or ".mask_token" in name
+            or ".cls_token" in name
+            or ".register_tokens" in name
+        ):
+            layer_id = 0
+        elif force_is_backbone and (
+            "pos_embed" in name
+            or "patch_embed" in name
+            or "mask_token" in name
+            or "cls_token" in name
+            or "register_tokens" in name
+        ):
+            layer_id = 0
+        elif ".blocks." in name and ".residual." not in name:
+            layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1
+        elif chunked_blocks and "blocks." in name and "residual." not in name:
+            layer_id = int(name[name.find("blocks.") :].split(".")[2]) + 1
+        elif "blocks." in name and "residual." not in name:
+            layer_id = int(name[name.find("blocks.") :].split(".")[1]) + 1
+
+    return lr_decay_rate ** (num_layers + 1 - layer_id)
+
+
+def get_params_groups_with_decay(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0):
+    chunked_blocks = False
+    if hasattr(model, "n_blocks"):
+        logger.info("chunked fsdp")
+        n_blocks = model.n_blocks
+        chunked_blocks = model.chunked_blocks
+    elif hasattr(model, "blocks"):
+        logger.info("first code branch")
+        n_blocks = len(model.blocks)
+    elif hasattr(model, "backbone"):
+        logger.info("second code branch")
+        n_blocks = len(model.backbone.blocks)
+    else:
+        logger.info("else code branch")
+        n_blocks = 0
+    all_param_groups = []
+
+    for name, param in model.named_parameters():
+        name = name.replace("_fsdp_wrapped_module.", "")
+        if not param.requires_grad:
+            continue
+        decay_rate = get_vit_lr_decay_rate(
+            name, lr_decay_rate, num_layers=n_blocks, force_is_backbone=n_blocks > 0, chunked_blocks=chunked_blocks
+        )
+        d = {"params": param, "is_last_layer": False, "lr_multiplier": decay_rate, "wd_multiplier": 1.0, "name": name}
+
+        if "last_layer" in name:
+            d.update({"is_last_layer": True})
+
+        if name.endswith(".bias") or "norm" in name or "gamma" in name:
+            d.update({"wd_multiplier": 0.0})
+
+        if "patch_embed" in name:
+            d.update({"lr_multiplier": d["lr_multiplier"] * patch_embed_lr_mult})
+
+        all_param_groups.append(d)
+        logger.info(f"""{name}: lr_multiplier: {d["lr_multiplier"]}, wd_multiplier: {d["wd_multiplier"]}""")
+
+    return all_param_groups
+
+
+def fuse_params_groups(all_params_groups, keys=("lr_multiplier", "wd_multiplier", "is_last_layer")):
+    fused_params_groups = defaultdict(lambda: {"params": []})
+    for d in all_params_groups:
+        identifier = ""
+        for k in keys:
+            identifier += k + str(d[k]) + "_"
+
+        for k in keys:
+            fused_params_groups[identifier][k] = d[k]
+        fused_params_groups[identifier]["params"].append(d["params"])
+
+    return fused_params_groups.values()
diff --git a/src/dinov2/utils/utils.py b/src/dinov2/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..68f8e2c3be5f780bbb7e00359b5ac4fd0ba0785f
--- /dev/null
+++ b/src/dinov2/utils/utils.py
@@ -0,0 +1,95 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import random
+import subprocess
+from urllib.parse import urlparse
+
+import numpy as np
+import torch
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+def load_pretrained_weights(model, pretrained_weights, checkpoint_key):
+    if urlparse(pretrained_weights).scheme:  # If it looks like an URL
+        state_dict = torch.hub.load_state_dict_from_url(pretrained_weights, map_location="cpu")
+    else:
+        state_dict = torch.load(pretrained_weights, map_location="cpu")
+    if checkpoint_key is not None and checkpoint_key in state_dict:
+        logger.info(f"Take key {checkpoint_key} in provided checkpoint dict")
+        state_dict = state_dict[checkpoint_key]
+    # remove `module.` prefix
+    state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
+    # remove `backbone.` prefix induced by multicrop wrapper
+    state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
+    msg = model.load_state_dict(state_dict, strict=False)
+    logger.info("Pretrained weights found at {} and loaded with msg: {}".format(pretrained_weights, msg))
+
+
+def fix_random_seeds(seed=31):
+    """
+    Fix random seeds.
+    """
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+def get_sha():
+    cwd = os.path.dirname(os.path.abspath(__file__))
+
+    def _run(command):
+        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
+
+    sha = "N/A"
+    diff = "clean"
+    branch = "N/A"
+    try:
+        sha = _run(["git", "rev-parse", "HEAD"])
+        subprocess.check_output(["git", "diff"], cwd=cwd)
+        diff = _run(["git", "diff-index", "HEAD"])
+        diff = "has uncommitted changes" if diff else "clean"
+        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
+    except Exception:
+        pass
+    message = f"sha: {sha}, status: {diff}, branch: {branch}"
+    return message
+
+
+class CosineScheduler(object):
+    def __init__(self, base_value, final_value, total_iters, warmup_iters=0, start_warmup_value=0, freeze_iters=0):
+        super().__init__()
+        self.final_value = final_value
+        self.total_iters = total_iters
+
+        freeze_schedule = np.zeros((freeze_iters))
+
+        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
+
+        iters = np.arange(total_iters - warmup_iters - freeze_iters)
+        schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters)))
+        self.schedule = np.concatenate((freeze_schedule, warmup_schedule, schedule))
+
+        assert len(self.schedule) == self.total_iters
+
+    def __getitem__(self, it):
+        if it >= self.total_iters:
+            return self.final_value
+        else:
+            return self.schedule[it]
+
+
+def has_batchnorms(model):
+    bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
+    for name, module in model.named_modules():
+        if isinstance(module, bn_types):
+            return True
+    return False
diff --git a/src/model_LN_prompt.py b/src/model_LN_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e509c1296934bfff35519ce62ba76f71797b496
--- /dev/null
+++ b/src/model_LN_prompt.py
@@ -0,0 +1,68 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchmetrics.functional import retrieval_average_precision
+import pytorch_lightning as pl
+
+from src.dinov2.models.vision_transformer import vit_base
+
+from functools import partial
+
+# from src.clip import clip
+from src.options import opts
+
+def freeze_model(m):
+    m.requires_grad_(False)
+
+def freeze_all_but_bn(m):
+    if not isinstance(m, torch.nn.LayerNorm):
+        if hasattr(m, 'weight') and m.weight is not None:
+            m.weight.requires_grad_(False)
+        if hasattr(m, 'bias') and m.bias is not None:
+            m.bias.requires_grad_(False)
+    else:
+        print("LayerNorm")
+
+class Model(pl.LightningModule):
+    def __init__(self):
+        super().__init__()
+
+        self.opts = opts
+
+        self.dino = vit_base(patch_size=14, block_chunks=0, init_values=1.0) 
+        print("self.dino", self.dino)
+
+        # Prompt Engineering
+        self.sk_prompt = nn.Parameter(torch.randn(self.opts.n_prompts, self.opts.prompt_dim))
+        self.img_prompt = nn.Parameter(torch.randn(self.opts.n_prompts, self.opts.prompt_dim))
+
+        self.distance_fn = lambda x, y: 1.0 - F.cosine_similarity(x, y)
+        self.loss_fn_triplet = nn.TripletMarginWithDistanceLoss(
+             distance_function=self.distance_fn, margin=0.2)
+        
+        self.emb_cos_loss = nn.CosineEmbeddingLoss(margin=0.2)
+
+        self.loss_kl = nn.KLDivLoss(reduction="batchmean", log_target=True)
+
+        self.best_metric = -1e3
+        # normalization layer for the representations z1 and z2
+        # self.bn = nn.BatchNorm1d(self.opts.prompt_dim, affine=False)
+
+    def configure_optimizers(self):
+        if self.opts.model_type == 'one_encoder':
+            model_params = list(self.dino.parameters())
+        else:
+            model_params = list(self.dino.parameters()) + list(self.clip_sk.parameters())
+
+        optimizer = torch.optim.Adam([
+            {'params': model_params, 'lr': self.opts.clip_LN_lr},
+            {'params': [self.sk_prompt] + [self.img_prompt], 'lr': self.opts.prompt_lr}])
+        return optimizer
+
+    def forward(self, data, dtype='image'):
+        if dtype == 'image':
+            feat = self.dino(data, prompt=self.img_prompt.expand(data.shape[0], -1, -1))
+        else:
+            feat = self.dino(data, prompt=self.sk_prompt.expand(data.shape[0], -1, -1))
+        return feat
\ No newline at end of file
diff --git a/src/options.py b/src/options.py
new file mode 100644
index 0000000000000000000000000000000000000000..4912a36ea94dc25438f03b971bcef3967da880db
--- /dev/null
+++ b/src/options.py
@@ -0,0 +1,23 @@
+import argparse
+
+parser = argparse.ArgumentParser(description='Sketch-based OD')
+
+parser.add_argument('--exp_name', type=str, default='LN_prompt')
+
+# ----------------------
+# Training Params
+# ----------------------
+
+parser.add_argument('--clip_lr', type=float, default=1e-4)
+parser.add_argument('--clip_LN_lr', type=float, default=1e-6)
+parser.add_argument('--prompt_lr', type=float, default=1e-4)
+parser.add_argument('--linear_lr', type=float, default=1e-4)
+parser.add_argument('--model_type', type=str, default='one_encoder', choices=['one_encoder', 'two_encoder'])
+
+# ----------------------
+# ViT Prompt Parameters
+# ----------------------
+parser.add_argument('--prompt_dim', type=int, default=768)
+parser.add_argument('--n_prompts', type=int, default=3)
+
+opts = parser.parse_args()
\ No newline at end of file