Inference Code Steps
Python1. First unzip the submission.zip file from https://drive.google.com/file/d/1zEWaW4dzsXauHGPL0lJOeeRDZ9wkXvVv/view?usp=sharing
2. Change directory to category2/ and run pip install - r requirements.txt to install packages and prepare MvTec LOCO dataset path
3. Now change directory to category2 and
Submision results can be seen by running python evaluation.py --module_path fsmodel --class_name AnomalyDetectionModel - -weights_path VA_GAN/exps/pretrained/visa_pretrained.pth --dataset_path ../../datasets/MvTec-LOCO --category pushpins
**Note If the mvtec_loco dataloader from anomalib gives error then please comment out these lines in anomalib library.**
anomalib/data/image/mvtec_loco.py from line 164 to 176.
2. Change directory to category2/ and run pip install - r requirements.txt to install packages and prepare MvTec LOCO dataset path
3. Now change directory to category2 and
Submision results can be seen by running python evaluation.py --module_path fsmodel --class_name AnomalyDetectionModel - -weights_path VA_GAN/exps/pretrained/visa_pretrained.pth --dataset_path ../../datasets/MvTec-LOCO --category pushpins
**Note If the mvtec_loco dataloader from anomalib gives error then please comment out these lines in anomalib library.**
anomalib/data/image/mvtec_loco.py from line 164 to 176.
python evaluation.py --module_path fsmodel --class_name AnomalyDetectionModel - -weights_path VA_GAN/exps/pretrained/visa_pretrained.pth --dataset_path ../../datasets/MvTec-LOCO --category pushpins
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