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gpu_id=0 | |
# Note: Since we have utilized half-precision (FP16) for training, the training process can occasionally be unstable. | |
# It is recommended to run the training process multiple times and choose the best model based on performance | |
# on the validation set as the final model. | |
# pre-trained on MVtec and colondb | |
CUDA_VISIBLE_DEVICES=$gpu_id python train.py --save_fig True --training_data mvtec colondb --testing_data visa | |
# pre-trained on Visa and Clinicdb | |
CUDA_VISIBLE_DEVICES=$gpu_id python train.py --save_fig True --training_data visa clinicdb --testing_data mvtec | |
# This model is pre-trained on all available data to create a powerful Zero-Shot Anomaly Detection (ZSAD) model for demonstration purposes. | |
CUDA_VISIBLE_DEVICES=$gpu_id python train.py --save_fig True \ | |
--training_data \ | |
br35h brain_mri btad clinicdb colondb \ | |
dagm dtd headct isic mpdd mvtec sdd tn3k visa \ | |
--testing_data mvtec | |