Table of Contents

• Introduction
• Requirements
• Installation
• Pretrained Models and Logs
• Data Preparation
• Training
• Evaluation
• Citations
• License
• Acknowledgements
• Contact

Introduction

This repository provides the code for the paper "FakeMix Augmentation Improves Transparent Object Detection".

Requirements

• python3
• PyTorch=1.1.0
• torchvision
• Pillow
• numpy
• pyyaml

Installation

Please make sure that there is at least one gpu when compiling. Then run:

• python3 setup.py develop

Pretrained Models and Logs

The pretrained models and logs can be fould here:

• Google Drive

• Baidu Drive with code: j3qp

Data Preparation

1. create dirs './datasets/Trans10K'
2. download the data from Trans10K.
3. put the train/validation/test data under './datasets/Trans10K'. The data Structure is shown below:

Trans10K/
├── test
│   ├── easy
│   └── hard
├── train
│   ├── images
│   └── masks
└── validation
    ├── easy
    └── hard

Demo

CUDA_VISIBLE_DEVICES=0 python3 -u ./tools/test_demo.py --config-file configs/trans10K/trans10K.yaml DEMO_DIR ./demo/imgs

Training

bash tools/dist_train.sh configs/trans10K/trans10K.yaml 8

Evaluation

CUDA_VISIBLE_DEVICES=0 python3 -u ./tools/test_translab.py --config-file configs/trans10K/trans10K.yaml 

Citations

Please cite our paper if the project helps.

@article{cao2021fakemix,
  title={FakeMix Augmentation Improves Transparent Object Detection},
    author={Cao, Yang and Zhang, Zhengqiang and Xie, Enze and Hou, Qibin and Zhao, Kai and Luo, Xiangui and Tuo, Jian},
      journal={arXiv preprint arXiv:2103.13279},
        year={2021}
        }

@misc{fanet,
    author = {Cao, Yang and Zhang, Zhengqiang},
    title = {fanet},
    howpublished = {\url{https://github.com/yangcao1996/fanet}},
    year ={2021}
}
        

License

For academic use, this project is licensed under the Apache 2.0 License

For commercial use, please contact the authors.

Acknowledgements

Our codes are mainly based on TransLab. Thanks to their wonderful works.

Contact

Any discussion is welcome. Please contact the authors:

Yang Cao: yangcao.cs@gmail.com

Zhengqiang Zhang: zhengqiang.zhang@hotmail.com