timm
/

Image Classification
timm
PyTorch
Safetensors
rwightman HF staff commited on
Commit
16a2474
1 Parent(s): 72fb0c4
Files changed (4) hide show
  1. README.md +128 -0
  2. config.json +35 -0
  3. model.safetensors +3 -0
  4. pytorch_model.bin +3 -0
README.md ADDED
@@ -0,0 +1,128 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - image-classification
4
+ - timm
5
+ library_name: timm
6
+ license: apache-2.0
7
+ datasets:
8
+ - imagenet-1k
9
+ - imagenet-22k
10
+ ---
11
+ # Model card for caformer_m36.sail_in22k_ft_in1k
12
+
13
+ A CAFormer (a MetaFormer) image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors.
14
+
15
+ ## Model Details
16
+ - **Model Type:** Image classification / feature backbone
17
+ - **Model Stats:**
18
+ - Params (M): 56.2
19
+ - GMACs: 13.3
20
+ - Activations (M): 50.5
21
+ - Image size: 224 x 224
22
+ - **Papers:**
23
+ - Metaformer baselines for vision: https://arxiv.org/abs/2210.13452
24
+ - **Original:** https://github.com/sail-sg/metaformer
25
+ - **Dataset:** ImageNet-1k
26
+ - **Pretrain Dataset:** ImageNet-22k
27
+
28
+ ## Model Usage
29
+ ### Image Classification
30
+ ```python
31
+ from urllib.request import urlopen
32
+ from PIL import Image
33
+ import timm
34
+
35
+ img = Image.open(urlopen(
36
+ 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
37
+ ))
38
+
39
+ model = timm.create_model('caformer_m36.sail_in22k_ft_in1k', pretrained=True)
40
+ model = model.eval()
41
+
42
+ # get model specific transforms (normalization, resize)
43
+ data_config = timm.data.resolve_model_data_config(model)
44
+ transforms = timm.data.create_transform(**data_config, is_training=False)
45
+
46
+ output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
47
+
48
+ top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
49
+ ```
50
+
51
+ ### Feature Map Extraction
52
+ ```python
53
+ from urllib.request import urlopen
54
+ from PIL import Image
55
+ import timm
56
+
57
+ img = Image.open(urlopen(
58
+ 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
59
+ ))
60
+
61
+ model = timm.create_model(
62
+ 'caformer_m36.sail_in22k_ft_in1k',
63
+ pretrained=True,
64
+ features_only=True,
65
+ )
66
+ model = model.eval()
67
+
68
+ # get model specific transforms (normalization, resize)
69
+ data_config = timm.data.resolve_model_data_config(model)
70
+ transforms = timm.data.create_transform(**data_config, is_training=False)
71
+
72
+ output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
73
+
74
+ for o in output:
75
+ # print shape of each feature map in output
76
+ # e.g.:
77
+ # torch.Size([1, 96, 56, 56])
78
+ # torch.Size([1, 192, 28, 28])
79
+ # torch.Size([1, 384, 14, 14])
80
+ # torch.Size([1, 576, 7, 7])
81
+
82
+ print(o.shape)
83
+ ```
84
+
85
+ ### Image Embeddings
86
+ ```python
87
+ from urllib.request import urlopen
88
+ from PIL import Image
89
+ import timm
90
+
91
+ img = Image.open(urlopen(
92
+ 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
93
+ ))
94
+
95
+ model = timm.create_model(
96
+ 'caformer_m36.sail_in22k_ft_in1k',
97
+ pretrained=True,
98
+ num_classes=0, # remove classifier nn.Linear
99
+ )
100
+ model = model.eval()
101
+
102
+ # get model specific transforms (normalization, resize)
103
+ data_config = timm.data.resolve_model_data_config(model)
104
+ transforms = timm.data.create_transform(**data_config, is_training=False)
105
+
106
+ output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
107
+
108
+ # or equivalently (without needing to set num_classes=0)
109
+
110
+ output = model.forward_features(transforms(img).unsqueeze(0))
111
+ # output is unpooled, a (1, 576, 7, 7) shaped tensor
112
+
113
+ output = model.forward_head(output, pre_logits=True)
114
+ # output is a (1, num_features) shaped tensor
115
+ ```
116
+
117
+ ## Model Comparison
118
+ Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
119
+
120
+ ## Citation
121
+ ```bibtex
122
+ @article{yu2022metaformer_baselines,
123
+ title={Metaformer baselines for vision},
124
+ author={Yu, Weihao and Si, Chenyang and Zhou, Pan and Luo, Mi and Zhou, Yichen and Feng, Jiashi and Yan, Shuicheng and Wang, Xinchao},
125
+ journal={arXiv preprint arXiv:2210.13452},
126
+ year={2022}
127
+ }
128
+ ```
config.json ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architecture": "caformer_m36",
3
+ "num_classes": 1000,
4
+ "num_features": 576,
5
+ "pretrained_cfg": {
6
+ "tag": "sail_in22k_ft_in1k",
7
+ "custom_load": false,
8
+ "input_size": [
9
+ 3,
10
+ 224,
11
+ 224
12
+ ],
13
+ "fixed_input_size": false,
14
+ "interpolation": "bicubic",
15
+ "crop_pct": 1.0,
16
+ "crop_mode": "center",
17
+ "mean": [
18
+ 0.485,
19
+ 0.456,
20
+ 0.406
21
+ ],
22
+ "std": [
23
+ 0.229,
24
+ 0.224,
25
+ 0.225
26
+ ],
27
+ "num_classes": 1000,
28
+ "pool_size": [
29
+ 7,
30
+ 7
31
+ ],
32
+ "first_conv": "stem.conv",
33
+ "classifier": "head.fc.fc2"
34
+ }
35
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cd5e957c47e8eea91ae66ecef8d87b1453003aec62bc8fd7161d621d69c97877
3
+ size 224860916
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:50f831141454b97e7a3432e417ccf8bb5675ea31e8031539873ee15ab2c00848
3
+ size 224988985