In this repository, the official example suggested to train is as below:
python trainer_cfa.py --class_name all --data_path [/path/to/dataset/] --cnn wrn50_2 --Rd False --size 224 --gamma_c 1 --gamma_d 1
But, if you do this, args.Rd always returns 'True'. So if you want to train in the condition of args.Rd = False, I suggest you run this code with the command as follows (just remove the Rd argument):
python trainer_cfa.py --class_name all --data_path [/path/to/dataset/] --cnn wrn50_2 --size 224 --gamma_c 1 --gamma_d 1
When I run this code with Rd argument that makes 'args.Rd' returns true, I got 90.6% for the Image AUROC for the screw data. But after making the 'args.Rd' to return False, I got 94.5% for the Image AUROC for the screw data.
In this repository, the official example suggested to train is as below: python trainer_cfa.py --class_name all --data_path [/path/to/dataset/] --cnn wrn50_2 --Rd False --size 224 --gamma_c 1 --gamma_d 1
But, if you do this, args.Rd always returns 'True'. So if you want to train in the condition of args.Rd = False, I suggest you run this code with the command as follows (just remove the Rd argument): python trainer_cfa.py --class_name all --data_path [/path/to/dataset/] --cnn wrn50_2 --size 224 --gamma_c 1 --gamma_d 1
When I run this code with Rd argument that makes 'args.Rd' returns true, I got 90.6% for the Image AUROC for the screw data. But after making the 'args.Rd' to return False, I got 94.5% for the Image AUROC for the screw data.
Thank you.