daijifeng001/R-FCN

Health v2 · maintenance only

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1248 stars · 533 forks observed · 2026-08-28

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

MATLAB implementation of R-FCN, a region-based fully convolutional object detection framework described in a NIPS 2016 paper. It builds on a custom Caffe branch with ResNet backbones and provides training, inference, and demo scripts for object detection on datasets like PASCAL VOC.

Use cases

• detect objects in images with a fully convolutional region-based detector
• train an object detection model on PASCAL VOC with ResNet backbones
• run the R-FCN demo to localize and classify objects in images
• reproduce NIPS 2016 R-FCN paper results
• compare R-FCN against Faster R-CNN baselines
• use a faster alternative to per-region subnetwork detectors

When to choose

• you need the original MATLAB R-FCN implementation for research reproduction
• you work in MATLAB with Caffe and NVIDIA GPUs
• you want an efficient fully convolutional object detector sharing computation across the whole image

When to avoid

• you want a maintained Python or MXNet implementation (use py-R-FCN or Deformable ConvNets instead)
• you need modern detection frameworks like PyTorch or TensorFlow
• you require end-to-end training with recent tooling

Facets

library · maturity maintenance

machine-learning computer-vision image-processing computer-vision deep-learning machine-learning windows object-detection r-fcn caffe resnet deep-learning research-code nips-2016 linux matlab gpu

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