Mask-RCNN-TF2

This is an implementation of Mask R-CNN on TensorFlow 2.2 and Keras 2.3.1. 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.

The repository includes:

• Example of training on the metal plate object dataset
• Source code of Mask R-CNN built on FPN and ResNet101.
• Training code for MS COCO
• Pre-trained weights for MS COCO
• Jupyter notebooks to visualize the detection pipeline at every step
• ParallelModel class for multi-GPU training

Training on Your Own Dataset - Google Colab Version

mask_rcnn_tf2.ipynb shows how to train Mask R-CNN on our own dataset.

Please consider downloading the following versions:

• pip uninstall tensorflow -y
• pip uninstall keras -y
• pip install tensorflow-gpu==2.2.0
• pip install keras==2.3.1
• pip3 install scikit-image==0.16.2
• pip3 install opencv-python

Training on Your Own Dataset - ML Cluster Version

Installation

1. Clone this repository

2. Install dependencies

   pip3 install -r requirements.txt

3. Run setup from the repository root directory

   python3 setup.py install

4. Download pre-trained COCO weights (mask_rcnn_coco.h5) from the releases page.

5. (Optional) To train or test on MS COCO install pycocotools from one of these repos. They are forks of the original pycocotools with fixes for Python3 and Windows (the official repo doesn't seem to be active anymore).

   • Linux: https://github.com/waleedka/coco
   • Windows: https://github.com/philferriere/cocoapi. You must have the Visual C++ 2015 build tools on your path (see the repo for additional details)