commit files to HF hub

README.md

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-Implementation of ResNet proposed in [Deep Residual Learning for Image
-Recognition](https://arxiv.org/abs/1512.03385)
-
- ``` python
- ResNet.resnet18()
- ResNet.resnet26()
- ResNet.resnet34()
- ResNet.resnet50()
- ResNet.resnet101()
- ResNet.resnet152()
- ResNet.resnet200()
-
- Variants (d) proposed in `Bag of Tricks for Image Classification with Convolutional Neural Networks <https://arxiv.org/pdf/1812.01187.pdf`_
-
- ResNet.resnet26d()
- ResNet.resnet34d()
- ResNet.resnet50d()
- # You can construct your own one by chaning `stem` and `block`
- resnet101d = ResNet.resnet101(stem=ResNetStemC, block=partial(ResNetBottleneckBlock, shortcut=ResNetShorcutD))
- ```
-
- Examples:
-
-  ``` python
-  # change activation
-  ResNet.resnet18(activation = nn.SELU)
-  # change number of classes (default is 1000 )
-  ResNet.resnet18(n_classes=100)
-  # pass a different block
-  ResNet.resnet18(block=SENetBasicBlock)
-  # change the steam
-  model = ResNet.resnet18(stem=ResNetStemC)
-  change shortcut
-  model = ResNet.resnet18(block=partial(ResNetBasicBlock, shortcut=ResNetShorcutD))
-  # store each feature
-  x = torch.rand((1, 3, 224, 224))
-  # get features
-  model = ResNet.resnet18()
-  # first call .features, this will activate the forward hooks and tells the model you'll like to get the features
-  model.encoder.features
-  model(torch.randn((1,3,224,224)))
-  # get the features from the encoder
-  features = model.encoder.features
-  print([x.shape for x in features])
-  #[torch.Size([1, 64, 112, 112]), torch.Size([1, 64, 56, 56]), torch.Size([1, 128, 28, 28]), torch.Size([1, 256, 14, 14])]
-  ```
-
- Args:
-
- :   in\_channels (int, optional): Number of channels in the input
-     Image (3 for RGB and 1 for Gray). Defaults to 3. n\_classes (int,
-     optional): Number of classes. Defaults to 1000.
+#ResNet