FrancescoSaverioZuppichini/BottleNeck-InvertedResidual-FusedMBConv-in-PyTorch
A little walk-trough different types of the block with their corresponding implementation in PyTorch
This project helps deep learning practitioners understand and implement various convolutional neural network (CNN) building blocks, like Residual, Bottleneck, and Inverted Residual connections, that are fundamental to architectures such as ResNet, MobileNet, and EfficientNet. It provides clear explanations and direct PyTorch code examples for these blocks, taking input tensors and outputting processed tensors according to the block's design. This is for machine learning engineers or researchers working on computer vision tasks.
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Use this if you need to understand and implement advanced CNN building blocks in PyTorch for creating or modifying image recognition models.
Not ideal if you are looking for a high-level library to build complete deep learning models without needing to delve into the specifics of block implementations.
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Jupyter Notebook
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Oct 14, 2021
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