Open Looksing opened 1 month ago
Hi, firstly thank you for your amazing work, I would like to use it to detect defects on complex surfaces, but no matter what weights(train_visa.pth or train_mvtec.pth) I use, the results are not good.For example,sometimes, surface components are mistaken for defects. In this case, how should I choose the auxiliary training set to train? Thank you so much, I am looking forward to your reply.
Hi, for certain complex product categories, zero-shot anomaly segmentation is challenging, as some anomalies can only be defined based on normal samples. Therefore, for the provided samples above, if practical application is desired, similar samples can be added to an auxiliary dataset for supervised prompt learning, which makes the training process highly efficient.
First of all, thank you for your outstanding work. As you pointed out, anomaly detection in complex scenarios, like the image Looksing uploaded, poses significant challenges for pretrained models, leading to the need for supervised prompt learning to mitigate suboptimal results. I have a few follow-up questions:
Hi, firstly thank you for your amazing work, I would like to use it to detect defects on complex surfaces, but no matter what weights(train_visa.pth or train_mvtec.pth) I use, the results are not good.For example,sometimes, surface components are mistaken for defects. In this case, how should I choose the auxiliary training set to train? Thank you so much, I am looking forward to your reply.