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Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks
The main features of facebookresearch/resnext are: Computer Vision, Image Classification Architectures.
Open-source alternatives to facebookresearch/resnext include: pytorch/vision — This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection… liuzhuang13/densenet — DenseNet is a computer vision model and convolutional neural network implementation designed for image recognition and… jongchan/attention-module — Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention… hujie-frank/senet — Squeeze-and-Excitation Networks. kaiminghe/resnet-1k-layers — Deep Residual Networks with 1K Layers. szagoruyko/wide-residual-networks — 3.8% and 18.3% on CIFAR-10 and CIFAR-100.
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
DenseNet is a computer vision model and convolutional neural network implementation designed for image recognition and classification tasks. It utilizes a densely connected network architecture where each layer is connected to every other layer to improve feature propagation. The implementation reduces the number of parameters while maintaining accuracy through a dense-connectivity pattern and layer-aggregation concatenation. It supports model construction using both standard and bottleneck-compressed architectures, with configurable network depth and growth rates to balance inference time an
