Hugging Face's logo

timm
/
eva02_small_patch14_336.mim_in22k_ft_in1k

Image Classification
timm
PyTorch
Safetensors
Model card Files Community
eva02_small_patch14_336.mim_in22k_ft_in1k
File size: 5,400 Bytes 
922d759
c43b522
 
922d759
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
 
---
license: mit
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- imagenet-22k
---
# Model card for eva02_small_patch14_336.mim_in22k_ft_in1k

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-1k 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): 22.1
  - GMACs: 15.5
  - Activations (M): 54.3
  - Image size: 336 x 336
- **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-1k

## 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_small_patch14_336.mim_in22k_ft_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(
    'eva02_small_patch14_336.mim_in22k_ft_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, 384) 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}}
}
```