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: Real-time Instance Segmentation
• YOLACT++: Better Real-time Instance Segmentation

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