tags:
- clip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: cc-by-nc-4.0
datasets:
- visheratin/laion-coco-nllb
Model Summary
NLLB-SigLIP-MRL is a model that combines a text encoder from the NLLB model and an image encoder from the SigLIP model. This allows us to extend the model capabilities to 201 languages of the Flores-200. This version of the model was trained using a variation of Matryoshka Representation learning to enable the generation of embeddings of sizes [32, 64, 128, 256, 512] in addition to the original 768. Based on the benchmarks below, embeddings of sizes 256 and 512 preserve 90%+ of the full embedding quality.
The full embedding model sets new state-of-the-art for multilingual image and text retrieval on both XTD10 and Crossmodal-3600.
How to use
Variable resolutions
If you want to use the model that supports variable embedding sizes, you can do it as follows:
!pip install -U transformers open_clip_torch
from transformers import AutoModel
from PIL import Image
import requests
import torch
model = AutoModel.from_pretrained("visheratin/nllb-siglip-mrl-base", device="cpu", trust_remote_code=True)
image_path = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
image = Image.open(requests.get(image_path, stream=True).raw)
class_options = ["бабочка", "butterfly", "kat"]
class_langs = ["rus_Cyrl", "eng_Latn", "afr_Latn"]
image_logits, text_logits = model.get_logits(
images=[image],
texts=class_options,
langs=class_langs,
resolution=512 # set resolution here or set `None` to use the original resolution
)
print(torch.softmax(image_logits, dim=1))
OpenCLIP
This model is also integrated into OpenCLIP so that you can use it as any other model:
!pip install -U open_clip_torch
from open_clip import create_model_from_pretrained, get_tokenizer
from PIL import Image
import requests
import torch
model, transform = create_model_from_pretrained("nllb-clip-base-siglip", "mrl", device="cuda")
tokenizer = get_tokenizer("nllb-clip-base-siglip")
class_options = ["бабочка", "butterfly", "kat"]
class_langs = ["rus_Cyrl", "eng_Latn", "afr_Latn"]
text_inputs = []
for i in range(len(class_options)):
tokenizer.set_language(class_langs[i])
text_inputs.append(tokenizer(class_options[i]))
text_inputs = torch.stack(text_inputs).squeeze(1).to("cuda")
image_path = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
image = Image.open(requests.get(image_path, stream=True).raw)
image_inputs = transform(image).unsqueeze(0).to("cuda")
with torch.inference_mode():
logits_per_image, logits_per_text = model.get_logits(image_inputs, text_inputs)
print(logits_per_image.softmax(dim=-1))
Acknowledgements
I thank ML Collective for providing Google Cloud compute resources.