Image Classification
timm
PyTorch
rdnet
DongHyunKim commited on
Commit
a6abb1f
1 Parent(s): 9fcc252

Upload rdnet_base.nv_1k.md

Browse files
Files changed (1) hide show
  1. rdnet_base.nv_1k.md +126 -0
rdnet_base.nv_1k.md ADDED
@@ -0,0 +1,126 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - image-classification
4
+ - timm
5
+ - rdnet
6
+ library_name: timm
7
+ datasets:
8
+ - imagenet-1k
9
+ ---
10
+ # Model card for rdnet_base.nv_in1k
11
+
12
+ A RDNet image classification model. Trained on ImageNet-1k, original torchvision weights.
13
+
14
+ ## Model Details
15
+ - **Model Type:** Image classification / feature backbone
16
+ - **Model Stats:**
17
+ - Imagenet-1k validation top-1 accuracy: 84.4%
18
+ - Params (M): 87
19
+ - GMACs: 15.4
20
+ - Image size: 224 x 224
21
+ - **Papers:**
22
+ - DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs: https://arxiv.org/abs/2403.19588
23
+ - **Dataset:** ImageNet-1k
24
+
25
+ ## Model Usage
26
+ ### Image Classification
27
+ ```python
28
+ from urllib.request import urlopen
29
+ from PIL import Image
30
+ import timm
31
+ import torch
32
+
33
+ img = Image.open(urlopen(
34
+ 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
35
+ ))
36
+
37
+ model = timm.create_model('rdnet_base.nv_in1k', pretrained=True)
38
+ model = model.eval()
39
+
40
+ # get model specific transforms (normalization, resize)
41
+ data_config = timm.data.resolve_model_data_config(model)
42
+ transforms = timm.data.create_transform(**data_config, is_training=False)
43
+
44
+ output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
45
+
46
+ top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
47
+ ```
48
+
49
+ ### Feature Map Extraction
50
+ ```python
51
+ from urllib.request import urlopen
52
+ from PIL import Image
53
+ import timm
54
+
55
+ img = Image.open(urlopen(
56
+ 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
57
+ ))
58
+
59
+ model = timm.create_model(
60
+ 'rdnet_base.nv_in1k',
61
+ pretrained=True,
62
+ features_only=True,
63
+ )
64
+ model = model.eval()
65
+
66
+ # get model specific transforms (normalization, resize)
67
+ data_config = timm.data.resolve_model_data_config(model)
68
+ transforms = timm.data.create_transform(**data_config, is_training=False)
69
+
70
+ output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
71
+
72
+ for o in output:
73
+ # print shape of each feature map in output
74
+ # e.g.:
75
+ # torch.Size([1, 64, 224, 224])
76
+ # torch.Size([1, 128, 112, 112])
77
+ # torch.Size([1, 256, 56, 56])
78
+ # torch.Size([1, 512, 28, 28])
79
+ # torch.Size([1, 512, 14, 14])
80
+ # torch.Size([1, 512, 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
+ 'rdnet_base.nv_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, 512, 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
+ ### Citation
118
+ ```
119
+ @misc{kim2024densenets,
120
+ title={DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs},
121
+ author={Donghyun Kim and Byeongho Heo and Dongyoon Han},
122
+ year={2024},
123
+ eprint={2403.19588},
124
+ archivePrefix={arXiv},
125
+ }
126
+ ```