CASIA-IVA-Lab/DANet

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2463 stars · 480 forks observed · 2026-08-28

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

DANet is the official PyTorch implementation of 'Dual Attention Network for Scene Segmentation' (CVPR 2019), which uses position and channel attention to integrate local features with global dependencies. It provides training and evaluation code with pretrained models for semantic segmentation benchmarks like Cityscapes, PASCAL Context, and COCO Stuff-10k.

Use cases

• run semantic segmentation on cityscapes images
• reproduce CVPR 2019 scene segmentation results
• train an attention-based segmentation model on my own dataset
• evaluate a pretrained DANet-101 model on cityscapes val set
• compare self-attention segmentation models
• segment street scenes for autonomous driving research

When to choose

• you need a proven attention-based semantic segmentation baseline with pretrained weights
• you want to reproduce or extend the DANet/DRANet paper results on Cityscapes
• you are researching self-attention mechanisms in dense prediction

When to avoid

• you need a maintained general-purpose segmentation toolkit (use MMSegmentation instead)
• you need real-time or lightweight segmentation on edge devices
• you work outside PyTorch or need modern architecture support out of the box

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision deep-learning machine-learning python semantic-segmentation scene-segmentation self-attention pytorch cvpr2019 cityscapes resnet gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="CASIA-IVA-Lab/DANet")