Jongchan/attention-module

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2230 stars · 405 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of two attention modules for convolutional neural networks: BAM (Bottleneck Attention Module) and CBAM (Convolutional Block Attention Module). It includes ImageNet training/validation scripts, ResNet50 examples, and pretrained checkpoints.

Use cases

• add attention modules to a CNN in PyTorch
• reproduce CBAM ImageNet training results
• get pretrained ResNet50+CBAM weights
• compare BAM vs CBAM attention blocks
• plug attention into ResNet for image classification

When to choose

• you need the official reference implementation of BAM or CBAM
• you want pretrained checkpoints to validate an attention-augmented ResNet
• you're doing computer vision research on attention mechanisms

When to avoid

• you need a maintained library with recent PyTorch support
• you want plug-and-play attention for non-ResNet architectures without adaptation
• you need production training pipelines rather than research code

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision deep-learning image-processing python pytorch attention-mechanism cbam bam image-classification research-code resnet linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Jongchan/attention-module")