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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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Usage

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Performance

Avg. Latency

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Alternatives to Faster R-CNN

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Meta
Mask R-CNN
Mask R-CNN is an instance segmentation model developed by Facebook AI Research (Meta), released in October 2017. It extends Faster R-CNN by adding a parallel branch that predicts binary segmentation masks for each detected object, independent of the classification and bounding box regression branches. A key contribution is RoIAlign, which replaces RoIPool with bilinear interpolation to preserve spatial correspondence between features and input pixels, significantly improving mask quality.Mask R-CNN achieves strong performance on the COCO instance segmentation benchmark and supports keypoint detection as an additional output head. It remains a foundational architecture in instance segmentation and is available through Meta's Detectron2 framework. The model is most appropriate for tasks requiring pixel-level object delineation, such as medical imaging, autonomous driving, and industrial inspection.
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DETR
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YOLO11
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Faster R-CNN License

MIT · Permissive license

Faster R-CNN is released under MIT, a permissive license. The Faster R-CNN license lets you use, modify, and sell work built on the model, with the copyright notice as the only real obligation and no requirement to open-source related code changes.

Commercial use
Permitted with no separate commercial license. No usage caps, revenue thresholds, or field-of-use limits apply to Faster R-CNN.
Modification
Permitted. You can fine-tune or rewrite Faster R-CNN and keep the result closed-source.
Redistribution
Permitted. Include the original copyright and permission notice in copies or substantial portions of the work.

MIT grants no explicit patent license and disclaims all warranties. If patent exposure is a concern for your deployment, review it with counsel before launch.

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

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.

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