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--- |
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license: mit |
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library_name: timm |
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tags: |
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- image-feature-extraction |
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- timm |
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--- |
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# Model card for eva02_large_patch14_224.mim_m38m |
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An EVA02 feature / representation model. Pretrained on Merged-38M (IN-22K, CC12M, CC3M, COCO (train), ADE20K (train), Object365, and OpenImages) with masked image modeling (using EVA-CLIP as a MIM teacher) by paper authors. |
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EVA-02 models are vision transformers with mean pooling, SwiGLU, Rotary Position Embeddings (ROPE), and extra LN in MLP (for Base & Large). |
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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. |
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## Model Details |
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- **Model Type:** Image classification / feature backbone |
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- **Model Stats:** |
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- Params (M): 303.3 |
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- GMACs: 81.1 |
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- Activations (M): 97.2 |
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- Image size: 224 x 224 |
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- **Papers:** |
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- EVA-02: A Visual Representation for Neon Genesis: https://arxiv.org/abs/2303.11331 |
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- EVA-CLIP: Improved Training Techniques for CLIP at Scale: https://arxiv.org/abs/2303.15389 |
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- **Original:** |
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- https://github.com/baaivision/EVA |
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- https://huggingface.co/Yuxin-CV/EVA-02 |
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- **Pretrain Dataset:** ImageNet-22k |
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## Model Usage |
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### Image Classification |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model('eva02_large_patch14_224.mim_m38m', pretrained=True) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
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top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) |
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``` |
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### Image Embeddings |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model( |
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'eva02_large_patch14_224.mim_m38m', |
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pretrained=True, |
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num_classes=0, # remove classifier nn.Linear |
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) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor |
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# or equivalently (without needing to set num_classes=0) |
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output = model.forward_features(transforms(img).unsqueeze(0)) |
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# output is unpooled, a (1, 257, 1024) shaped tensor |
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output = model.forward_head(output, pre_logits=True) |
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# output is a (1, num_features) shaped tensor |
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``` |
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## Model Comparison |
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Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). |
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|model |top1 |top5 |param_count|img_size| |
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|-----------------------------------------------|------|------|-----------|--------| |
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|eva02_large_patch14_448.mim_m38m_ft_in22k_in1k |90.054|99.042|305.08 |448 | |
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|eva02_large_patch14_448.mim_in22k_ft_in22k_in1k|89.946|99.01 |305.08 |448 | |
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|eva_giant_patch14_560.m30m_ft_in22k_in1k |89.792|98.992|1014.45 |560 | |
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|eva02_large_patch14_448.mim_in22k_ft_in1k |89.626|98.954|305.08 |448 | |
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|eva02_large_patch14_448.mim_m38m_ft_in1k |89.57 |98.918|305.08 |448 | |
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|eva_giant_patch14_336.m30m_ft_in22k_in1k |89.56 |98.956|1013.01 |336 | |
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|eva_giant_patch14_336.clip_ft_in1k |89.466|98.82 |1013.01 |336 | |
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|eva_large_patch14_336.in22k_ft_in22k_in1k |89.214|98.854|304.53 |336 | |
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|eva_giant_patch14_224.clip_ft_in1k |88.882|98.678|1012.56 |224 | |
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|eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12 |448 | |
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|eva_large_patch14_336.in22k_ft_in1k |88.652|98.722|304.53 |336 | |
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|eva_large_patch14_196.in22k_ft_in22k_in1k |88.592|98.656|304.14 |196 | |
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|eva02_base_patch14_448.mim_in22k_ft_in1k |88.23 |98.564|87.12 |448 | |
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|eva_large_patch14_196.in22k_ft_in1k |87.934|98.504|304.14 |196 | |
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|eva02_small_patch14_336.mim_in22k_ft_in1k |85.74 |97.614|22.13 |336 | |
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|eva02_tiny_patch14_336.mim_in22k_ft_in1k |80.658|95.524|5.76 |336 | |
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## Citation |
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```bibtex |
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@article{EVA02, |
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title={EVA-02: A Visual Representation for Neon Genesis}, |
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author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue}, |
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journal={arXiv preprint arXiv:2303.11331}, |
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year={2023} |
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} |
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``` |
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```bibtex |
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@article{EVA-CLIP, |
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title={EVA-02: A Visual Representation for Neon Genesis}, |
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author={Sun, Quan and Fang, Yuxin and Wu, Ledell and Wang, Xinlong and Cao, Yue}, |
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journal={arXiv preprint arXiv:2303.15389}, |
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year={2023} |
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} |
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``` |
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```bibtex |
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@misc{rw2019timm, |
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author = {Ross Wightman}, |
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title = {PyTorch Image Models}, |
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year = {2019}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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doi = {10.5281/zenodo.4414861}, |
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howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} |
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} |
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``` |
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