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Mask R-CNN

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Automatically detect and segment objects in images, generating high-quality segmentation masks.

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Inputs: image
Type
Saas
Company
Matterport
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About Mask R-CNN

Mask R-CNN is a powerful deep learning-based object detection and segmentation tool. It is designed to accurately identify and segment objects within an image, including people, animals, furniture, and other objects of interest. With Mask R-CNN, users can detect and segment objects in real-time, with a single pass of the network. It is relatively easy to use and requires minimal setup, making it ideal for both experienced and novice users.Mask R-CNN is powered by a convolutional neural network (CNN) and is capable of handling a variety of challenging tasks, such as object detection, instance segmentation, and semantic segmentation. It is also able to detect and segment multiple objects in an image simultaneously. Additionally, it can generate high-quality segmentation masks for each detected object, making it an ideal choice for many computer vision tasks.

Key Features

Automatically detect objects in an image.
Accurately segment objects with a single pass.
Generate high-quality segmentation masks for each detected object.

Pros & Cons

Pros
  • Fully open-source with a permissive license (MIT)
  • Comprehensive documentation and step-by-step Jupyter notebooks for education
  • Pre-trained on COCO, enabling out-of-the-box segmentation on 80+ classes
  • Supports multi-GPU training for faster experimentation
  • Easy to extend with custom backbones or datasets
  • Actively maintained with 25.6k stars on GitHub
Cons
  • Requires significant computational resources (GPU recommended) for training
  • Steep learning curve for users new to deep learning or Keras/TensorFlow
  • Dependent on older TensorFlow 1.x / Keras API; may need adaptation for TF2
  • Not optimized for real-time inference on CPU
  • Limited to instance segmentation; does not include panoptic segmentation

Best For

Automatically detect objects in an image.Accurately segment objects with a single pass.Generate high-quality segmentation masks for each detected object.

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FAQ

What is Mask R-CNN?
Mask R-CNN is a deep learning model for object detection and instance segmentation. It extends Faster R-CNN by adding a branch for predicting segmentation masks on each Region of Interest (RoI).
What backbone does this implementation use?
The implementation uses a Feature Pyramid Network (FPN) with a ResNet101 backbone.
Can I train Mask R-CNN on my own dataset?
Yes, the repository includes an example Jupyter notebook (train_shapes.ipynb) that demonstrates training on a custom toy dataset (Shapes). You can adapt it for your own dataset.
Does this implementation support multi-GPU training?
Yes, it includes a ParallelModel class that enables multi-GPU training.
Are pre-trained weights available?
Yes, pre-trained weights for the MS COCO dataset are provided, allowing you to segment objects out of the box.