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README.md

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+# resnext50_32x4d
+Implementation of ResNetXt proposed in [\"Aggregated Residual
+Transformation for Deep Neural
+Networks\"](https://arxiv.org/pdf/1611.05431.pdf)
+
+ Create a default model
+
+ ``` python
+ ResNetXt.resnext50_32x4d()
+ ResNetXt.resnext101_32x8d()
+ # create a resnetxt18_32x4d
+ ResNetXt.resnet18(block=ResNetXtBottleNeckBlock, groups=32, base_width=4)
+ ```
+
+ Examples:
+
+ :   ``` python
+     # change activation
+     ResNetXt.resnext50_32x4d(activation = nn.SELU)
+     # change number of classes (default is 1000 )
+     ResNetXt.resnext50_32x4d(n_classes=100)
+     # pass a different block
+     ResNetXt.resnext50_32x4d(block=SENetBasicBlock)
+     # change the initial convolution
+     model = ResNetXt.resnext50_32x4d
+     model.encoder.gate.conv1 = nn.Conv2d(3, 64, kernel_size=3)
+     # store each feature
+     x = torch.rand((1, 3, 224, 224))
+     model = ResNetXt.resnext50_32x4d()
+     # 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])]
+     ```
+
+