matterport/Mask_RCNN

Health v2 · maintenance only

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25567 stars · 11610 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Python implementation of the Mask R-CNN model for object detection and instance segmentation, built on Keras and TensorFlow with a ResNet101 backbone and Feature Pyramid Network. It includes COCO pre-trained weights, training code, multi-GPU support, and Jupyter notebooks for visualization and custom dataset training.

Use cases

• segment objects in images with instance masks
• train a custom object detection model on my own dataset
• run pre-trained Mask R-CNN on COCO images
• detect and segment multiple object instances in a photo
• visualize the detection pipeline step by step
• fine-tune Mask R-CNN for my research project

When to choose

• you need instance segmentation (masks per object), not just bounding boxes
• you want readable, well-documented research code that is easy to extend
• you want pre-trained COCO weights and notebooks to inspect the model internals
• you are working with TensorFlow 1.x/Keras and multi-GPU training

When to avoid

• you need the latest architectures like YOLO, Detectron2, or transformer-based detectors
• you need TensorFlow 2.x or PyTorch support
• you need production-grade performance or active maintenance
• you only need simple object detection without segmentation masks

Facets

library · maturity maintenance

machine-learning computer-vision image-processing deep-learning computer-vision machine-learning deep-learning image-processing python cross-platform mask-rcnn object-detection instance-segmentation keras tensorflow coco resnet feature-pyramid-network gpu

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