rbgirshick/rcnn

Health v2 · maintenance only

Adoption not part of the score

2416 stars · 969 forks observed · 2026-08-28

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

The original R-CNN (Region-based Convolutional Neural Networks) object detection system from UC Berkeley, released as research code accompanying CVPR 2014 and PAMI papers. It combines bottom-up region proposals with CNN features for state-of-the-art object detection on PASCAL VOC and ImageNet.

Use cases

• reproduce the original R-CNN object detection results on PASCAL VOC
• study the historical R-CNN implementation for research or education
• run region-based CNN object detection in MATLAB with Caffe
• compare classic R-CNN baselines against modern detectors
• access pretrained R-CNN models for VOC 2007/2010/2012 and ILSVRC 2013

When to choose

• you need the exact original R-CNN code to reproduce or cite the CVPR 2014 paper
• you are studying the evolution of CNN-based object detectors
• your workflow is MATLAB-based and you need the historical implementation

When to avoid

• you want a modern, maintained object detector - use Fast/Faster R-CNN or newer frameworks instead
• you need production-ready or actively supported code
• you cannot work with MATLAB and legacy Caffe dependencies

Facets

library · maturity abandoned

machine-learning deep-learning computer-vision image-processing computer-vision machine-learning deep-learning cpp object-detection rcnn pascal-voc caffe historical research-code linux matlab

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markdown · JSON · MCP: product_card(name="rbgirshick/rcnn")