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Concrete Crack Detection With YOLACT

This repository contain code to train YOLACT netrwork for concrete crack detection and segementation. Please refer to follwing papers to learn about YOLACT algorithm

YOLACT++ (v1.2) released! (Changelog)

YOLACT++'s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO's test-dev (check out our journal paper here).

In order to use YOLACT++, make sure you compile the DCNv2 code. (See Installation)

Installation

  • Clone this repository and enter it: 
    git clone https://github.com/dbolya/yolact.git
cd yolact
  • Set up the environment using one of the following methods:
    • Using Anaconda
      • Run conda env create -f environment.yml
    • Manually with pip
      • Set up a Python3 environment (e.g., using virtenv).
      • Install Pytorch 1.0.1 (or higher) and TorchVision.
      • Install some other packages: 
        # Cython needs to be installed before pycocotools
pip install cython
pip install opencv-python pillow pycocotools matplotlib