I trained a model to eval another category ....

[simo-an]

I train a model of wood using the command:

python run_training.py  --head_layer 2 --type wood

Then in the evaluate process, is use the model to evaluate the type of bottle , I got a surprisingly excellent result:

AUC: 0.9949454642192073

Is there any problem here?

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I just used the following hard code to evaluate (in eval.py line 258):

roc_auc = eval_model(model_name, 'bottle', save_plots=args.save_plots, device=device, head_layer=args.head_layer, density=density())

Thanks!

[Runinho]

I just used the following hard code to evaluate (in eval.py line 258):

roc_auc = eval_model(model_name, 'bottle', save_plots=args.save_plots, device=device, head_layer=args.head_layer, density=density())

Thanks!

You hard coded bottle as the second argument. So I suspect you get the AUC of that class.

Instead of hardcoding you could also use the eval script:

python eval.py --head_layer 2 --type wood