commit files to HF hub

README.md

@@ -1,52 +1,52 @@
 Implementation of ResNet proposed in [Deep Residual Learning for Image
 Recognition](https://arxiv.org/abs/1512.03385)
 
-> ``` {.sourceCode .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:
->
-> > ``` {.sourceCode .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.
+ ``` 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.