An EVA image classification model. Pretrained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher) and fine-tuned on ImageNet-22k then on ImageNet-1k by paper authors.
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.
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('eva_large_patch14_196.in22k_ft_in22k_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)
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(
'eva_large_patch14_196.in22k_ft_in22k_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, 197, 1024) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
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 |
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
journal={arXiv preprint arXiv:2211.07636},
year={2022}
}
@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}}
}