Mask-RCNN
Mask R-CNN
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, boundingbox object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition.
Implementations
Detectron
Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.
- Mask R-CNN -- Marr Prize at ICCV 2017
- RetinaNet -- Best Student Paper Award at ICCV 2017
- Faster R-CNN
- RPN
- Fast R-CNN
- R-FCN
using the following backbone network architectures:
- ResNeXt{50,101,152}
- ResNet{50,101,152}
- Feature Pyramid Networks (with ResNet/ResNeXt)
- VGG16
Models: https://github.com/facebookresearch/Detectron/tree/master/detectron/modeling
MX Mask R-CNN
An MXNet implementation of Mask R-CNN.
This repository is based largely on the mx-rcnn implementation of Faster RCNN available here.
Mask R-CNN for Object Detection and Segmentation
This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
Getting Started
demo.ipynb Is the easiest way to start. It shows an example of using a model pre-trained on MS COCO to segment objects in your own images. It includes code to run object detection and instance segmentation on arbitrary images.
train_shapes.ipynb shows how to train Mask R-CNN on your own dataset. This notebook introduces a toy dataset (Shapes) to demonstrate training on a new dataset.
(model.py, utils.py, config.py): These files contain the main Mask RCNN implementation.
inspect_data.ipynb. This notebook visualizes the different pre-processing steps to prepare the training data.
inspect_model.ipynb This notebook goes in depth into the steps performed to detect and segment objects. It provides visualizations of every step of the pipeline.
inspect_weights.ipynb This notebooks inspects the weights of a trained model and looks for anomalies and odd patterns.
Model: https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/model.py
