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timm/eva02_tiny_patch14_224.mim_in22k

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Model Card

Model card for eva02tinypatch14224.mimin22k

An EVA02 feature / representation model. Pretrained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher) 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): 5.5
—GMACs: 1.7
—Activations (M): 9.1
—Image size: 224 x 224
—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

Model Usage

Image Classification

python

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_tiny_patch14_224.mim_in22k', 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

python

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_tiny_patch14_224.mim_in22k',
    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, 257, 192) 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
eva02largepatch14448.mimm38mftin22k_in1k	90.054	99.042	305.08	448
eva02largepatch14448.mimin22kftin22k_in1k	89.946	99.01	305.08	448
evagiantpatch14560.m30mftin22kin1k	89.792	98.992	1014.45	560
eva02largepatch14448.mimin22kftin1k	89.626	98.954	305.08	448
eva02largepatch14448.mimm38mftin1k	89.57	98.918	305.08	448
evagiantpatch14336.m30mftin22kin1k	89.56	98.956	1013.01	336
evagiantpatch14336.clipft_in1k	89.466	98.82	1013.01	336
evalargepatch14336.in22kftin22kin1k	89.214	98.854	304.53	336
evagiantpatch14224.clipft_in1k	88.882	98.678	1012.56	224
eva02basepatch14448.mimin22kftin22k_in1k	88.692	98.722	87.12	448
evalargepatch14336.in22kft_in1k	88.652	98.722	304.53	336
evalargepatch14196.in22kftin22kin1k	88.592	98.656	304.14	196
eva02basepatch14448.mimin22kftin1k	88.23	98.564	87.12	448
evalargepatch14196.in22kft_in1k	87.934	98.504	304.14	196
eva02smallpatch14336.mimin22kftin1k	85.74	97.614	22.13	336
eva02tinypatch14336.mimin22kftin1k	80.658	95.524	5.76	336

Citation

bibtex

@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}
}

bibtex

@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}
}

bibtex

@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}}
}