Introduction of eva_giant_patch14_560.m30m_ft_in22k_in1k
Model Details of eva_giant_patch14_560.m30m_ft_in22k_in1k
Model card for eva_giant_patch14_560.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
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_560.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
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_560.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, 1601, 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
@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}}
}
Runs of timm eva_giant_patch14_560.m30m_ft_in22k_in1k on huggingface.co
171.0K
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24-hour runs
11.3K
3-day runs
30.4K
7-day runs
85.2K
30-day runs
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