Trans-DCN

A High-Efficiency and Adaptive Deep Network for Bridge Cable Surface Defect Segmentation

Introduction

This is a PyTorch(1.8.0) implementation of Trans-DCN. It can use Modified backbone as train.py mentioned. Currently, we can train Trans-DCN using Pascal VOC 2012, SBD and Cityscapes datasets.

Installation

The code was tested with Anaconda and Python 3.8. After installing the Anaconda environment:

0. Clone the repo:

   git clone https://github.com/hzh1231/Trans_dcn.git
   cd Trans_dcn

1. Install dependencies:

   For PyTorch dependency, see pytorch.org for more details.

   For custom dependencies:

   pip install matplotlib pillow tensorboardX tqdm

Training

Follow steps below to train your model:

0. Configure your dataset path in mypath.py.

1. Input arguments: (see full input arguments via python train.py --help):

python train.py

Testing

Follow steps below to test your model:

0. Configure your test data (images) path in predict.py.

1. Input arguments: (see full input arguments via python predict.py --help):

python predict.py