KaihuaTang/Scene-Graph-Benchmark.pytorch

Health v2 · maintenance only

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Adoption not part of the score

1195 stars · 236 forks observed · 2026-08-28

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

A PyTorch codebase for Scene Graph Generation (SGG) built on maskrcnn-benchmark, implementing methods from the CVPR 2020 paper 'Unbiased Scene Graph Generation from Biased Training'. It supports training, benchmarking with standard SGG metrics, and extracting/visualizing scene graphs on custom images.

Use cases

• generate scene graphs from images
• benchmark scene graph generation models
• run unbiased SGG from biased training
• visualize detected scene graphs on custom images
• train relationship prediction models in PyTorch
• evaluate SGG with Recall@K and mean Recall metrics
• extract scene graphs from a custom dataset

When to choose

• you need a standard benchmark codebase for scene graph generation research
• you want to reproduce or extend CVPR 2020 unbiased SGG methods
• you need scene graph extraction and visualization on your own images

When to avoid

• you need production-ready object detection rather than research SGG code
• you require a modern maintained framework rather than a 2020-era PyTorch codebase
• you work outside Python/PyTorch environments

Facets

library · maturity maintenance

machine-learning computer-vision image-processing benchmarking data-visualization computer-vision deep-learning machine-learning image-processing python scene-graph-generation pytorch cvpr-2020 relationship-prediction maskrcnn-benchmark sgg research-code linux gpu

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