Resnet26d

🚀 ResNet26D

This project is an implementation of ResNet proposed in Deep Residual Learning for Image Recognition, which is used for image classification tasks.

🚀 Quick Start

This project provides various ResNet model construction methods, allowing you to easily create different variants of ResNet models.

✨ Features

• Multiple Model Variants: Supports multiple ResNet models such as resnet18, resnet26, resnet34, etc., as well as their d variants.
• Customizable Parameters: You can customize the activation function, number of classes, block type, stem, and shortcut of the model.
• Feature Extraction: It is possible to extract features from the encoder of the model.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

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))

Advanced Usage

# 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])]

📚 Documentation

No detailed documentation is provided in the original document, so this section is skipped.

🔧 Technical Details

No technical details are provided in the original document, so this section is skipped.

📄 License

This project is licensed under the Apache-2.0 license.