|
--- |
|
tags: |
|
- image-classification |
|
- timm |
|
library_tag: timm |
|
license: mit |
|
datasets: |
|
- imagenet-22k |
|
- imagenet-22k |
|
--- |
|
# Model card for eva02_base_patch14_448.mim_in22k_ft_in22k |
|
|
|
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-22k 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): 103.1 |
|
- GMACs: 107.1 |
|
- Activations (M): 259.2 |
|
- 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-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_base_patch14_448.mim_in22k_ft_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_base_patch14_448.mim_in22k_ft_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, 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](https://github.com/huggingface/pytorch-image-models/tree/main/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 |
|
```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}} |
|
} |
|
``` |
|
|