shikishima-TasakiLab/Involution-PyTorch
Unofficial PyTorch reimplemention of the paper "Involution: Inverting the Inherence of Convolution for Visual Recognition" [CVPR 2021].
This project offers an alternative to standard convolution layers used in deep learning models for visual recognition tasks. It helps researchers and practitioners experimenting with novel neural network architectures by providing an Involution2d module. This module takes in multi-channel image data (tensors) and produces processed feature maps, which can then be used in subsequent layers of a computer vision model. This is for AI/ML researchers and deep learning engineers building and evaluating custom computer vision models.
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Use this if you are a deep learning researcher or engineer exploring new architectural components for computer vision models, specifically looking to replace or augment traditional convolutional layers.
Not ideal if you are looking for a pre-trained model or a high-level API for common computer vision tasks without delving into custom network architectures.
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Last pushed
Jul 13, 2021
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