Squeeze-and-Excitation Networks
Principalele funcționalități ale hujie-frank/senet sunt: Attention Mechanisms, Computer Vision, Computer Vision Research, Image Classification Architectures.
Alternativele open-source pentru hujie-frank/senet includ: jongchan/attention-module — Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention… zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… implus/sknet. facebookresearch/resnext — Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks. facebookresearch/detectandtrack — The implementation of an algorithm presented in the CVPR18 paper: "Detect-and-Track: Efficient Pose Estimation in… kaiminghe/resnet-1k-layers — Deep Residual Networks with 1K Layers.
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and
The implementation of an algorithm presented in the CVPR18 paper: "Detect-and-Track: Efficient Pose Estimation in Videos"