jwyang/faster-rcnn.pytorch

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

7858 stars · 2294 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A pure PyTorch implementation of Faster R-CNN for object detection, supporting multi-image batch training and multi-GPU training with several ROI pooling methods. It was the first open-source object detection repo with multi-GPU support but is now explicitly unmaintained.

Use cases

• train a faster r-cnn object detection model in pytorch
• run object detection on custom images with a pretrained model
• train faster r-cnn on pascal voc or coco with multiple gpus
• compare roi pooling, roi align, and roi crop implementations
• learn how faster r-cnn works by reading a pure pytorch codebase

When to choose

• you need a simple, readable pure-PyTorch Faster R-CNN reference implementation
• you want a lightweight playground for experimenting with R-CNN internals
• you are reproducing older research that builds on this codebase

When to avoid

• you need an actively maintained or up-to-date detection framework
• you want modern architectures like Mask R-CNN, DETR, or YOLO
• you are starting a new production object detection project

Facets

library · maturity abandoned

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning python object-detection faster-rcnn pytorch rpn roi-pooling multi-gpu-training gpu linux

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markdown · JSON · MCP: product_card(name="jwyang/faster-rcnn.pytorch")