Few-Shot Semantic Segmentation for Industrial Defect Recognition

This is the implementation of the paper ["Few-Shot Semantic Segmentation for Industrial Defect Recognition"].

Implemented on Python 3.8 and Pytorch 1.9. The main structure of the network is as follows:

<p align="middle"> <img src="./info/fig1.jpg"> </p>

Requirements

• Python 3.8
• PyTorch 1.13
• cuda 11.7
• opencv 4.3
• tensorboard 2.5.1

Preparing Industrial Datasets

Download from [ScienceDB].

Testing

Pretrained models are available on our [ScienceDB].

Set the parameters in test.py and execute: python test.py

Example of qualitative results (5-shot and 1-shot):

<p align="middle"> <img src="info/result.jpg"> </p>

Acknowledgment

Thanks to Juhong Min, Dahyun Kang and Minsu Ch for their contributions, much of our code is based on their shared HSNet.

BibTeX

If you use this code for your research, please consider citing:

@InProceedings{
    title={Few-Shot Semantic Segmentation for Industrial Defect Recognition},
    author={Xiangwen Shi, Shaobing Zhang, Miao Cheng, Lian He, Zhe Cui, Xianghong Tang},
}