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Mask R-CNN for Object Detection and Segmentation

This instruction indicates the installation steps of MASKRCNN used in the CapSal model. Please refer to the original repository if you have any questions.

The repository includes:

  • Source code of CapSal based on ResNet101
  • Training code on COCO-CapSal
  • Pre-trained weights for CapSal

Getting Started

The codes required in the CapSal model are stored in the CapSal. To begin with, you should first install the requirements for the MaskRCNN benchmark.

Requirements

Python 2.7 , TensorFlow 1.4.1, Keras 2.1.4 and other common packages listed in requirements.txt.

MS COCO Requirements:

To train or test on COCO-CapSal, you'll also need:

If you use Docker, the code has been verified to work on this Docker container.

Installation

  1. Install dependencies

    pip install -r requirements.txt

  2. Clone this repository

  3. Run setup from the repository root directory

    python 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).