metadata

license: mit
library_name: timm
tags:
  - image-classification
  - timm
datasets:
  - imagenet-1k
  - imagenet-22k

Model card for eva02_base_patch14_448.mim_in22k_ft_in1k

An EVA02 image classification model. Pretrained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher) and fine-tuned on ImageNet-1k by paper authors.

EVA-02 models are vision transformers with mean pooling, SwiGLU, Rotary Position Embeddings (ROPE), and extra LN in MLP (for Base & Large).

NOTE: timm checkpoints are float32 for consistency with other models. Original checkpoints are float16 or bfloat16 in some cases, see originals if that's preferred.

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 87.1
- GMACs: 107.1
- Activations (M): 259.1
- Image size: 448 x 448
Papers:
- EVA-02: A Visual Representation for Neon Genesis: https://arxiv.org/abs/2303.11331
- EVA-CLIP: Improved Training Techniques for CLIP at Scale: https://arxiv.org/abs/2303.15389
Original:
- https://github.com/baaivision/EVA
- https://huggingface.co/Yuxin-CV/EVA-02
Pretrain Dataset: ImageNet-22k
Dataset: ImageNet-1k

Model Usage

Image Classification

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('eva02_base_patch14_448.mim_in22k_ft_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Image Embeddings

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'eva02_base_patch14_448.mim_in22k_ft_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1025, 768) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

Model Comparison

Explore the dataset and runtime metrics of this model in timm model results.

model	top1	top5	param_count	img_size
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k	90.054	99.042	305.08	448
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k	89.946	99.01	305.08	448
eva_giant_patch14_560.m30m_ft_in22k_in1k	89.792	98.992	1014.45	560
eva02_large_patch14_448.mim_in22k_ft_in1k	89.626	98.954	305.08	448
eva02_large_patch14_448.mim_m38m_ft_in1k	89.57	98.918	305.08	448
eva_giant_patch14_336.m30m_ft_in22k_in1k	89.56	98.956	1013.01	336
eva_giant_patch14_336.clip_ft_in1k	89.466	98.82	1013.01	336
eva_large_patch14_336.in22k_ft_in22k_in1k	89.214	98.854	304.53	336
eva_giant_patch14_224.clip_ft_in1k	88.882	98.678	1012.56	224
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k	88.692	98.722	87.12	448
eva_large_patch14_336.in22k_ft_in1k	88.652	98.722	304.53	336
eva_large_patch14_196.in22k_ft_in22k_in1k	88.592	98.656	304.14	196
eva02_base_patch14_448.mim_in22k_ft_in1k	88.23	98.564	87.12	448
eva_large_patch14_196.in22k_ft_in1k	87.934	98.504	304.14	196
eva02_small_patch14_336.mim_in22k_ft_in1k	85.74	97.614	22.13	336
eva02_tiny_patch14_336.mim_in22k_ft_in1k	80.658	95.524	5.76	336

Citation

@article{EVA02,
  title={EVA-02: A Visual Representation for Neon Genesis},
  author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
  journal={arXiv preprint arXiv:2303.11331},
  year={2023}
}

@article{EVA-CLIP,
  title={EVA-02: A Visual Representation for Neon Genesis},
  author={Sun, Quan and Fang, Yuxin and Wu, Ledell and Wang, Xinlong and Cao, Yue},
  journal={arXiv preprint arXiv:2303.15389},
  year={2023}
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}