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Azure

Microsoft: Faster R-CNN

Faster R-CNN Overview

Faster R-CNN is an object detection model introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun at Microsoft Research, published at NIPS in June 2015. It advances upon Fast R-CNN and R-CNN by introducing the Region Proposal Network (RPN), a fully convolutional network that shares features with the detection network and generates object proposals at negligible additional cost. This makes Faster R-CNN the first near-real-time deep learning object detector based on region proposals.

Faster R-CNN achieves strong detection accuracy on PASCAL VOC and MS COCO at the time of release. It remains a widely referenced architecture in computer vision research and is available through Meta's Detectron2 framework as a maintained PyTorch implementation. It is most appropriate for offline or server-side inference tasks where accuracy is prioritized over latency, as its two-stage pipeline carries higher inference cost than single-stage detectors.

Faster R-CNN Details & Performance

Details

Resources

GitHubColabTutorialVideo

Vision Tasks

Object Detection

Features

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Faster R-CNN License

MIT

License terms and commercial-use guidance for Faster R-CNN.

This model is released under the MIT License, a short and permissive open-source license that allows commercial use, modification, and redistribution.

Read the full MIT license ↗

Yes. Under the terms of the MIT license, you can freely use this model for commercial purposes. You must retain the copyright notice and license text when redistributing.

License information is provided as a guide and is not legal advice.