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README.md

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+# efficientnet_b3
+Implementation of EfficientNet proposed in [EfficientNet: Rethinking
+Model Scaling for Convolutional Neural
+Networks](https://arxiv.org/abs/1905.11946)
+
+ ![image](https://github.com/FrancescoSaverioZuppichini/glasses/blob/develop/docs/_static/images/EfficientNet.png?raw=true)
+
+ The basic architecture is similar to MobileNetV2 as was computed by
+ using [Progressive Neural Architecture
+ Search](https://arxiv.org/abs/1905.11946) .
+
+ The following table shows the basic architecture
+ (EfficientNet-efficientnet\_b0):
+
+ ![image](https://github.com/FrancescoSaverioZuppichini/glasses/blob/develop/docs/_static/images/EfficientNetModelsTable.jpeg?raw=true)
+
+ Then, the architecture is scaled up from
+ [-efficientnet\_b0]{.title-ref} to [-efficientnet\_b7]{.title-ref}
+ using compound scaling.
+
+ ![image](https://github.com/FrancescoSaverioZuppichini/glasses/blob/develop/docs/_static/images/EfficientNetScaling.jpg?raw=true)
+
+ ``` python
+ EfficientNet.efficientnet_b0()
+ EfficientNet.efficientnet_b1()
+ EfficientNet.efficientnet_b2()
+ EfficientNet.efficientnet_b3()
+ EfficientNet.efficientnet_b4()
+ EfficientNet.efficientnet_b5()
+ EfficientNet.efficientnet_b6()
+ EfficientNet.efficientnet_b7()
+ EfficientNet.efficientnet_b8()
+ EfficientNet.efficientnet_l2()
+ ```
+
+ Examples:
+
+  ``` python
+  EfficientNet.efficientnet_b0(activation = nn.SELU)
+  # change number of classes (default is 1000 )
+  EfficientNet.efficientnet_b0(n_classes=100)
+  # pass a different block
+  EfficientNet.efficientnet_b0(block=...)
+  # store each feature
+  x = torch.rand((1, 3, 224, 224))
+  model = EfficientNet.efficientnet_b0()
+  # 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, 32, 112, 112]), torch.Size([1, 24, 56, 56]), torch.Size([1, 40, 28, 28]), torch.Size([1, 80, 14, 14])]
+  ```
+
+