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timm/
eva_giant_patch14_336.m30m_ft_in22k_in1k

Name: eva_giant_patch14_336.m30m_ft_in22k_in1k
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Model card for eva_giant_patch14_336.m30m_ft_in22k_in1k

An EVA image classification model. Pretrained on Merged-30M (ImageNet-22K, CC12M, CC3M, Object365, COCO (train), ADE20K (train)) with masked image modeling (using OpenAI CLIP-L 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.

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 1013.0
- GMACs: 620.6
- Activations (M): 550.7
- Image size: 336 x 336
Papers:
- EVA: Exploring the Limits of Masked Visual Representation Learning at Scale: https://arxiv.org/abs/2211.07636
Pretrain Dataset:
- Merged-30M
- ImageNet-22k
Dataset: ImageNet-1k
Original:
- https://github.com/baaivision/EVA
- https://huggingface.co/BAAI/EVA

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('eva_giant_patch14_336.m30m_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)

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(
    'eva_giant_patch14_336.m30m_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, 577, 1408) 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

bibtex

@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}
}