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Meta: Mask R-CNN

Mask R-CNN Overview

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.

Mask R-CNN Details & Performance

Details

Resources

GitHubPaperColabTutorial

Vision Tasks

Object DetectionInstance SegmentationKeypoint Detection

Features

Foundation Vision

Usage

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Mask R-CNN License

MIT

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

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