| --- |
| license: apache-2.0 |
| tags: |
| - image-classification |
| datasets: |
| - imagenet |
| --- |
| # resnet34 |
| 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])] |
| ``` |
|
|
| |
