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- ---
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- license: apache-2.0
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- tags:
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- - image-classification
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- - pytorch
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- - onnx
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- datasets:
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- - imagenette
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- ---
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-
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-
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- # ResNet-18 model
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-
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- Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ResNet architecture was introduced in [this paper](https://arxiv.org/pdf/1512.03385.pdf).
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-
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-
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- ## Model description
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-
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- The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
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-
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-
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- ## Installation
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-
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- ### Prerequisites
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-
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- Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
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-
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- ### Latest stable release
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-
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- You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
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-
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- ```shell
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- pip install pylocron
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- ```
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-
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- or using [conda](https://anaconda.org/frgfm/pylocron):
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-
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- ```shell
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- conda install -c frgfm pylocron
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- ```
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-
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- ### Developer mode
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-
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- Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
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-
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- ```shell
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- git clone https://github.com/frgfm/Holocron.git
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- pip install -e Holocron/.
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- ```
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-
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-
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- ## Usage instructions
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-
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- ```python
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- from PIL import Image
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- from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
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- from torchvision.transforms.functional import InterpolationMode
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- from holocron.models import model_from_hf_hub
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-
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- model = model_from_hf_hub("frgfm/resnet18").eval()
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-
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- img = Image.open(path_to_an_image).convert("RGB")
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-
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- # Preprocessing
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- config = model.default_cfg
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- transform = Compose([
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- Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
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- PILToTensor(),
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- ConvertImageDtype(torch.float32),
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- Normalize(config['mean'], config['std'])
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- ])
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-
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- input_tensor = transform(img).unsqueeze(0)
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-
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- # Inference
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- with torch.inference_mode():
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- output = model(input_tensor)
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- probs = output.squeeze(0).softmax(dim=0)
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- ```
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-
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-
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- ## Citation
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-
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- Original paper
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-
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- ```bibtex
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- @article{DBLP:journals/corr/HeZRS15,
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- author = {Kaiming He and
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- Xiangyu Zhang and
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- Shaoqing Ren and
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- Jian Sun},
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- title = {Deep Residual Learning for Image Recognition},
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- journal = {CoRR},
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- volume = {abs/1512.03385},
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- year = {2015},
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- url = {http://arxiv.org/abs/1512.03385},
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- eprinttype = {arXiv},
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- eprint = {1512.03385},
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- timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
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- biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
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- bibsource = {dblp computer science bibliography, https://dblp.org}
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- }
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- ```
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-
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- Source of this implementation
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-
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- ```bibtex
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- @software{Fernandez_Holocron_2020,
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- author = {Fernandez, François-Guillaume},
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- month = {5},
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- title = {{Holocron}},
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- url = {https://github.com/frgfm/Holocron},
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- year = {2020}
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- }
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- ```
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - image-classification
5
+ - pytorch
6
+ - onnx
7
+ datasets:
8
+ - frgfm/imagenette
9
+ ---
10
+
11
+
12
+ # ResNet-18 model
13
+
14
+ Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ResNet architecture was introduced in [this paper](https://arxiv.org/pdf/1512.03385.pdf).
15
+
16
+
17
+ ## Model description
18
+
19
+ The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
20
+
21
+
22
+ ## Installation
23
+
24
+ ### Prerequisites
25
+
26
+ Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
27
+
28
+ ### Latest stable release
29
+
30
+ You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
31
+
32
+ ```shell
33
+ pip install pylocron
34
+ ```
35
+
36
+ or using [conda](https://anaconda.org/frgfm/pylocron):
37
+
38
+ ```shell
39
+ conda install -c frgfm pylocron
40
+ ```
41
+
42
+ ### Developer mode
43
+
44
+ Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
45
+
46
+ ```shell
47
+ git clone https://github.com/frgfm/Holocron.git
48
+ pip install -e Holocron/.
49
+ ```
50
+
51
+
52
+ ## Usage instructions
53
+
54
+ ```python
55
+ from PIL import Image
56
+ from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
57
+ from torchvision.transforms.functional import InterpolationMode
58
+ from holocron.models import model_from_hf_hub
59
+
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+ model = model_from_hf_hub("frgfm/resnet18").eval()
61
+
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+ img = Image.open(path_to_an_image).convert("RGB")
63
+
64
+ # Preprocessing
65
+ config = model.default_cfg
66
+ transform = Compose([
67
+ Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
68
+ PILToTensor(),
69
+ ConvertImageDtype(torch.float32),
70
+ Normalize(config['mean'], config['std'])
71
+ ])
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+
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+ input_tensor = transform(img).unsqueeze(0)
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+
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+ # Inference
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+ with torch.inference_mode():
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+ output = model(input_tensor)
78
+ probs = output.squeeze(0).softmax(dim=0)
79
+ ```
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+
81
+
82
+ ## Citation
83
+
84
+ Original paper
85
+
86
+ ```bibtex
87
+ @article{DBLP:journals/corr/HeZRS15,
88
+ author = {Kaiming He and
89
+ Xiangyu Zhang and
90
+ Shaoqing Ren and
91
+ Jian Sun},
92
+ title = {Deep Residual Learning for Image Recognition},
93
+ journal = {CoRR},
94
+ volume = {abs/1512.03385},
95
+ year = {2015},
96
+ url = {http://arxiv.org/abs/1512.03385},
97
+ eprinttype = {arXiv},
98
+ eprint = {1512.03385},
99
+ timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
100
+ biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
101
+ bibsource = {dblp computer science bibliography, https://dblp.org}
102
+ }
103
+ ```
104
+
105
+ Source of this implementation
106
+
107
+ ```bibtex
108
+ @software{Fernandez_Holocron_2020,
109
+ author = {Fernandez, François-Guillaume},
110
+ month = {5},
111
+ title = {{Holocron}},
112
+ url = {https://github.com/frgfm/Holocron},
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+ year = {2020}
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+ }
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+ ```