Hugging Face's logo

timm
/
test_vit.r160_in1k

Image Classification
timm
PyTorch
Safetensors
Model card Files Community
test_vit.r160_in1k
File size: 5,310 Bytes 
4432504
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e3bfcf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4432504
 
 
 
 
 
 
 
 
 
 
 
 
 
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
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
 
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for test_vit.r160_in1k

A very small test Vision Transformer image classification model for testing and sanity checks. Trained on ImageNet-1k by Ross Wightman.

## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 0.4
  - GMACs: 0.0
  - Activations (M): 0.3
  - Image size: 160 x 160
- **Dataset:** ImageNet-1k
- **Papers:**
  - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
- **Original:** https://github.com/huggingface/pytorch-image-models

## 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('test_vit.r160_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)
```

### Feature Map Extraction
```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(
    'test_vit.r160_in1k',
    pretrained=True,
    features_only=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

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 64, 10, 10])
    #  torch.Size([1, 64, 10, 10])
    #  torch.Size([1, 64, 10, 10])

    print(o.shape)
```

### 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(
    'test_vit.r160_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, 101, 64) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```

## Model Comparison
### By Top-1

|model                           |img_size|top1  |top5  |param_count|
|--------------------------------|--------|------|------|-----------|
|test_convnext3.r160_in1k        |192     |54.558|79.356|0.47       |
|test_convnext2.r160_in1k        |192     |53.62 |78.636|0.48       |
|test_convnext2.r160_in1k        |160     |53.51 |78.526|0.48       |
|test_convnext3.r160_in1k        |160     |53.328|78.318|0.47       |
|test_convnext.r160_in1k         |192     |48.532|74.944|0.27       |
|test_nfnet.r160_in1k            |192     |48.298|73.446|0.38       |
|test_convnext.r160_in1k         |160     |47.764|74.152|0.27       |
|test_nfnet.r160_in1k            |160     |47.616|72.898|0.38       |
|test_efficientnet.r160_in1k     |192     |47.164|71.706|0.36       |
|test_efficientnet_evos.r160_in1k|192     |46.924|71.53 |0.36       |
|test_byobnet.r160_in1k          |192     |46.688|71.668|0.46       |
|test_efficientnet_evos.r160_in1k|160     |46.498|71.006|0.36       |
|test_efficientnet.r160_in1k     |160     |46.454|71.014|0.36       |
|test_byobnet.r160_in1k          |160     |45.852|70.996|0.46       |
|test_efficientnet_ln.r160_in1k  |192     |44.538|69.974|0.36       |
|test_efficientnet_gn.r160_in1k  |192     |44.448|69.75 |0.36       |
|test_efficientnet_ln.r160_in1k  |160     |43.916|69.404|0.36       |
|test_efficientnet_gn.r160_in1k  |160     |43.88 |69.162|0.36       |
|test_vit2.r160_in1k             |192     |43.454|69.798|0.46       |
|test_resnet.r160_in1k           |192     |42.376|68.744|0.47       |
|test_vit2.r160_in1k             |160     |42.232|68.982|0.46       |
|test_vit.r160_in1k              |192     |41.984|68.64 |0.37       |
|test_resnet.r160_in1k           |160     |41.578|67.956|0.47       |
|test_vit.r160_in1k              |160     |40.946|67.362|0.37       |

## Citation
```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}}
}
```