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SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects

<a href="https://arxiv.org/abs/2311.06504"><img src="https://img.shields.io/badge/arXiv-2311.06504-b31b1b.svg" height=22.5></a> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/github/license/WU-CVGL/BAD-NeRF" height=22.5></a>

We address the challenge of detecting object defects through the self-supervised learning approach of solving the jigsaw puzzle problem.

Results

Dependencies

Since I did this project a long time ago, there may be some potential issues with environmental dependencies.

• Tested with Python 3.8
• Pytorch v1.6.0

Dateset

• Dataset : MvTec AD

Run Training

• python train.py --obj=cable --lambda_value=1 --D=64 --epoches=400 --lr=1e-4 --gpu=0

Run Affinity Testing

• python test.py --obj=cable --gpu=0
• enc.load(obj, N) N is the serial number of the obtained training weight file

Anomaly maps

• python heat_map.py --obj=cable
• enc.load(obj, N) N is the serial number of the obtained training weight file

Details:

• The input of the network should be 256x256
• data.npy contains the relative positions and their reference numbers.