| | --- |
| | license: apache-2.0 |
| | tags: |
| | - image-classification |
| | datasets: |
| | - imagenet |
| | --- |
| | # resnet50 |
| | 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])] |
| | ``` |
| |
|
| | |
