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本项目实现了一种基于 VAE-CycleGAN 的图像重建无监督缺陷检测算法。该算法结合了变分自编码器 (VAE) 和 CycleGAN 的优势,无需标注数据即可检测图像中的缺陷/异常。This project implements an unsupervised defect detection algorithm for image reconstruction based on VAE-CycleGAN. This algorithm combines the advantages of variational autoencoders (VAE) and CycleGAN to detect defects in images without any supervision.

License: GNU General Public License v3.0

Python 100.00%
ai chinese defect-detection english pytorch unsupervised vae-gan

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unsupervised-defect-detection-project-based-on-vae-gan-architecture's Issues

环境依赖及一些问题

大佬你好:我们最近也遇到负样本不足的情况,想参考此项目思路进行实践,能否提供下程序的环境和相关包的版本,给个requirements list。

另外还有以下几个问题:
1、在readme中第一步是使用 CutTarget.py 文件来调用 Segment Anything Model (SAM) 自动分割图像中感兴趣的对象。但是在mian中有使用了一个PCBDiscriminator这个判别器(不知道跟TargetDiscriminator有什么区别?),这个也是通过VAE_GAN_train这个训练出来的么?
2、在第二步中使用 VAE_GAN_train.py 脚本在分割后的图像上训练 VAE-CycleGAN 模型。所以训练前是否是需要通过CutTarget预处理样本?那么项目的实施步骤是不是这样的:

  • [1 ] - 一开始需要先预处理下初始样本,比如把标准正样本的PCB部分剪切出来作为原始样本?先训练一遍后,得到项目中的:Discriminator_5.pth这个模型后,
  • [2 ] - 再通过CutTarget.py批量的进行图像分割,并且把分割的图像放到项目中cut_imgs文件夹,进行VAE_GAN_train训练。
  • [3 ] - 最后在项目使用的过程是通过main程序进行:先通过CutTarget切割出检测样本,在通过VAE_GAN生成图片,之后在进行样本和生成结果的比对。

不知我理解的对不对?还请不吝赐教!
感谢!

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