Tune parameters to get best results.

[Tekno-H]

Hello Denis,
I have been studying and adjusting this great repository to fit my needs in defect detection.
I have trained several models successfully(using the mobilenetv3_large backbone) and achieved good results. However, I have eliminated some parts of your code, including the snippets that calculate the "seg_threshold" parameter from the ground truth.
Therefore, I am choosing it by hand (through trial&error), and although the results are OK, I think they can be further improved.

My questions are:
1- How can I choose a value for "seg_threshold" reliably in my given case?
2- What parameters do you recommend fine-tuning when training a new model (knowing that I have a good and balanced dataset of the same product but with different colours)?
3- My last question is related to exporting the model to "onnx" format, do you have any comments on how to achieve that? Do you plan on adding that capability?
4- When should I stop training ?

Thank you in advance, your work is truly inspiring.

[gudovskiy]

1. Could you take some part of you train data to calculate optimal threshold? or do cross-validation?
2. I didn't try ONNX conversion. Did you look at https://github.com/openvinotoolkit/anomalib ?
3. Overfitting can happen when training for a long time. May be it is related to where is checkpoint? pretrained weights to reproduce your results? #1, where you can select a subset of your data for validation to avoid overfitting

[Tekno-H]

Thank you for your reply,

1. Could you take some part of you train data to calculate optimal threshold? or do cross-validation?

This is exacly what I ended up doing

2. I didn't try ONNX conversion. Did you look at https://github.com/openvinotoolkit/anomalib ?

I checked it and replied on the other thread

3. Overfitting can happen when training for a long time. May be it is related to [where is checkpoint? pretrained weights to reproduce your results? #1](https://github.com/gudovskiy/cflow-ad/issues/1), where you can select a subset of your data for validation to avoid overfitting

Thanks I will check it.