s9xie/hed

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1925 stars · 534 forks observed · 2026-08-28

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

Reference implementation of Holistically-Nested Edge Detection (HED), a deep learning edge and boundary detection model built on a modified Caffe framework with fully convolutional networks. It includes pretrained models, training, and testing code for reproducing benchmark results on BSDS500 and NYU Depth.

Use cases

• detect edges in images with a deep learning model
• reproduce HED results on the BSDS500 benchmark
• train an edge detection network on custom data
• extract object boundaries from images
• compare edge detection algorithms against HED baselines

When to choose

• you need state-of-the-art learned edge/boundary detection rather than classical operators like Canny
• you want pretrained weights and reproducible benchmark results
• your stack already uses Caffe

When to avoid

• you need a maintained framework on modern PyTorch/TensorFlow stacks
• you want a plug-and-play production library rather than research code
• you cannot build legacy Caffe dependencies

Facets

library · maturity maintenance

computer-vision image-processing deep-learning computer-vision image-processing deep-learning cpp python edge-detection caffe fully-convolutional-networks research-code boundary-detection linux

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