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README.md

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+# resnet34d
+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])]
+  ```
+
+ 

pytorch_model.bin

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