problem about load checkpoints about ddad HOT 9 CLOSED

Hi,

Could you please merely remove the ".zip" extension from the file named "2000" and check if it works properly? Without unzipping the file.

from ddad.

I do not find a checkpoints of feature_extractor named feat{DA_chp} in my downloaded files. Whether the fine-tuned model is not released, and should I fine-tune a feature_extractor myself?

I will release the checkpoints in the near future.

Prior to evaluation, it is necessary to fine-tune the feature extractor. To accomplish this, navigate to the config.yaml file and adjust the settings as outlined in the repository tables. Set DA_epochs and DA_chp based on the feature extractor epochs, and configure w with the corresponding value from the table.

After configuring the values in the config file, execute the following code to fine-tune the feature extractor. This process may take some time to complete:

Once the fine-tuning is done, you can proceed with model evaluation:
python main.py --domain_adaptation True

Now you can evaluate the model.
python main.py --eval True

If you wish to obtain information on misclassified samples and visualize reconstructions, you can customize the values of misclassifications and visualisation in the config file accordingly.
Please let me know if you face any other problems.

from ddad.

thank you! it works for me. However i encountered another problem when i load checkpoints of feature_extractor in 36 line of ddad.py (feature_extractor = domain_adaptation(self.unet, self.config, fine_tune=False) ). There is the notice as follow:

I do not find a checkpoints of feature_extractor named feat{DA_chp} in my downloaded files. Whether the fine-tuned model is not released, and should I fine-tune a feature_extractor myself?

from ddad.

I do not find a checkpoints of feature_extractor named feat{DA_chp} in my downloaded files. Whether the fine-tuned model is not released, and should I fine-tune a feature_extractor myself?

I will release the checkpoints in the near future.

Prior to evaluation, it is necessary to fine-tune the feature extractor. To accomplish this, navigate to the config.yaml file and adjust the settings as outlined in the repository tables. Set DA_epochs and DA_chp based on the feature extractor epochs, and configure w with the corresponding value from the table.

After configuring the values in the config file, execute the following code to fine-tune the feature extractor. This process may take some time to complete:

Once the fine-tuning is done, you can proceed with model evaluation: python main.py --domain_adaptation True

Now you can evaluate the model. python main.py --eval True

If you wish to obtain information on misclassified samples and visualize reconstructions, you can customize the values of misclassifications and visualisation in the config file accordingly. Please let me know if you face any other problems.

fine, thank you very much

from ddad.

Hi, i have fine-turn the feature extractor on hezelnut and screw. But the I-AUROC/P-AUROC is (96.4,99.3) on screw, and (99.8,98.2) on hazelnut. I set (load_chp=2000, DA_epochs=30, DA_chp=4, w=2 and w_DA=3) for screw and (load_chp=2000, DA_epochs=30, DA_chp=3, w=5 and w_DA=3) for hazelnut.

I set the corresponding parameters (w, and DA_chp) according to the readme, and set DA_epochs=30 and w_DA=3 on two categories. Is there any problem？

from ddad.

Apologies for the late response. I just cloned the code and conducted tests on both categories. Results are consistent with the reported ones. It's a bit challenging for me to pinpoint why you might be obtaining slightly different answers. Could you confirm that you're using the most recent version of our code?

To enhance usability, I plan to publish feature checkpoints over the weekend.

from ddad.

Also to decrease the fine-tuning time you can set the value of DA_epochs similar to DA_chp. It is not required to fine-tune for 30 epochs as it may be time-consuming.

from ddad.

Thank you very well. Sorry for not checking the issue for a long time. I certainty used most recent version. I think the only difference is batchsize(I set it as 12 because of limited GPU memory). I think it is the reason. Looking forward the checkpoints.

from ddad.

True, changing batch size will change the results. I have uploaded the MVTec checkpoints and will upload VisA checkpoints tomorrow.

from ddad.