Care sunt principalele funcționalități ale jongchan/attention-module?

Principalele funcționalități ale jongchan/attention-module sunt: Attention Mechanisms, Computer Vision, Image Classification Architectures.

Care sunt câteva alternative open-source pentru jongchan/attention-module?

Alternativele open-source pentru jongchan/attention-module includ: hujie-frank/senet — Squeeze-and-Excitation Networks. kaiminghe/resnet-1k-layers — Deep Residual Networks with 1K Layers. facebookresearch/resnext — Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks. liuzhuang13/densenet — DenseNet is a computer vision model and convolutional neural network implementation designed for image recognition and… implus/sknet. pytorch/vision — This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection…

Attention Module - add attention blocks

Name: jongchan/attention-module
Author: Jongchan

Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"

Features

Attention Mechanisms - Convolutional block and bottleneck attention modules.

Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"

Features

Attention Mechanisms - Convolutional block and bottleneck attention modules.

hujie-frank/SENet

3,641Vezi pe GitHub

Squeeze-and-Excitation Networks

implus/SKNet

0Vezi pe GitHub

facebookresearch/ResNeXt

1,926Vezi pe GitHub

Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks

KaimingHe/resnet-1k-layers

936Vezi pe GitHub

Deep Residual Networks with 1K Layers