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README.md

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+# regnetx_016
+Implementation of RegNet proposed in [Designing Network Design
+Spaces](https://arxiv.org/abs/2003.13678)
+
+ The main idea is to start with a high dimensional search space and
+ iteratively reduce the search space by empirically apply constrains
+ based on the best performing models sampled by the current search
+ space.
+
+ The resulting models are light, accurate, and faster than
+ EfficientNets (up to 5x times!)
+
+ For example, to go from $AnyNet_A$ to $AnyNet_B$ they fixed the
+ bottleneck ratio $b_i$ for all stage $i$. The following table shows
+ all the restrictions applied from one search space to the next one.
+
+ ![image](https://github.com/FrancescoSaverioZuppichini/glasses/blob/develop/docs/_static/images/RegNetDesignSpaceTable.png?raw=true)
+
+ The paper is really well written and very interesting, I highly
+ recommended read it.
+
+ ``` python
+ ResNet.regnetx_002()
+ ResNet.regnetx_004()
+ ResNet.regnetx_006()
+ ResNet.regnetx_008()
+ ResNet.regnetx_016()
+ ResNet.regnetx_040()
+ ResNet.regnetx_064()
+ ResNet.regnetx_080()
+ ResNet.regnetx_120()
+ ResNet.regnetx_160()
+ ResNet.regnetx_320()
+ # Y variants (with SE)
+ ResNet.regnety_002()
+ # ...
+ ResNet.regnetx_320()
+
+ You can easily customize your model
+ ```
+
+ Examples:
+
+  ``` python
+  # change activation
+  RegNet.regnetx_004(activation = nn.SELU)
+  # change number of classes (default is 1000 )
+  RegNet.regnetx_004(n_classes=100)
+  # pass a different block
+  RegNet.regnetx_004(block=RegNetYBotteneckBlock)
+  # change the steam
+  model = RegNet.regnetx_004(stem=ResNetStemC)
+  change shortcut
+  model = RegNet.regnetx_004(block=partial(RegNetYBotteneckBlock, shortcut=ResNetShorcutD))
+  # store each feature
+  x = torch.rand((1, 3, 224, 224))
+  # get features
+  model = RegNet.regnetx_004()
+  # 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, 32, 56, 56]), torch.Size([1, 64, 28, 28]), torch.Size([1, 160, 14, 14])]
+  ```
+
+