Comments (2)
For now, the anomanly_detection.py works per damage at the time (ex. bottle folder as input), whereas train.py uses all mvtec at once. We are going to optimize it soon to work for whole classes at once.
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Hello, First of all, thank you for your neat work! I managed to run your code on the bottle class of the Mvtec dataset by running the command:
python train.py --dataset_path /home/username1/data/datasets/mvtec/bottle/train --num_class 3
However when I tried to run the anomaly_detection.py, using the following command:
python anomaly_detection.py --checkpoint ./tb_logs/exp1/version_3/checkpoints --data /data/username1/datasets/mvtec/bottle/test
I did not obtain any results, the problem is that the
all_checkpoint
list in the main part of theanomaly_detection.py
contains only the checkpoint I specified in the command above, which makes me wonder whether I am doing something wrong when specifying the command above and also I wonder whether you have a different dataset organization on your side. Because for now, I am having the following organization (the original):mvtec │ readme. │ license.txt │ └─── bottle │ │ readme.txt │ │ lisence.txt │ │ │ └─── ground_truth │ | └─── broken_large │ | └─── broken_small │ | └─── contamination │ | | └─── train | | └─── good | | | 000.png | | | ... │ | | └─── test | | └─── broken_large | | | | 000.png | | | | ... | | └─── broken_small | | | | 000.png | | | | ... | | └─── contamination | | | | 000.png | | | | ... | | └─── good | | | | 000.png | | | | ... └─── cable | ... └─── zipper
Can you please tell me whether you used a different organization? Because when I look at
mvtec_anomaly_detection
function I see that you build a path using the defect name + "train" and you do the same for "test". Thank you for your help.
Hello, How did you solve this problem?
did you change the folders?
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Related Issues (17)
- I want to predict single image ,can you teach me what shoulde I do ? HOT 3
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