knazeri/edge-connect

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2620 stars · 528 forks observed · 2026-08-28

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

EdgeConnect is a PyTorch implementation of a two-stage generative adversarial model for image inpainting, published at ICCV 2019. It first hallucinates edges of missing image regions with an edge generator network, then fills the regions using those edges as structural priors.

Use cases

• fill in missing or masked regions of photos
• remove unwanted objects from images
• repair old or damaged photographs
• inpaint irregular holes in images with realistic structure
• reproduce research results from the EdgeConnect ICCV 2019 paper
• train a custom inpainting model on Places2 or CelebA

When to choose

• you need research-grade image inpainting with strong structural detail in filled regions
• you want a PyTorch codebase with pre-trained models for Places2 and CelebA
• you have an NVIDIA GPU and want to train or fine-tune an inpainting GAN
• you need to handle both regular and irregular missing-region masks

When to avoid

• you need a production-ready, actively maintained tool with a polished API or GUI
• you want inpainting on CPU or without CUDA-capable hardware
• you need a license-clear solution for commercial use (license is non-standard)
• you need text-guided or diffusion-based inpainting rather than mask-based inpainting

Facets

library · maturity maintenance

image-processing computer-vision deep-learning machine-learning computer-vision image-processing deep-learning machine-learning python cross-platform image-inpainting gan pytorch edge-prediction iccv-2019 research-code canny-edge-detection gpu linux

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markdown · JSON · MCP: product_card(name="knazeri/edge-connect")