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seominseok0429 / Real-world-Anomaly-Detection-in-Surveillance-Videos-pytorch

Real-world Anomaly Detection in Surveillance Videos- pytorch Re-implementation
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Evaluation Problems #16

Open Peggy118 opened 2 years ago

Peggy118 commented 2 years ago

Hi, thanks for your amazing work!

I have been trying to plot the ROC curve for the performance evaluation, but I am confused about how the auc is calculated in the test.py. Could you kindly explain your method? For example, what does the number 140 mean in the equation auc/140 in the python file test.py?

`score_list3 = np.concatenate((score_list, score_list2), axis=0) gt_list3 = np.concatenate((gt_list, gt_list2), axis=0)

fpr, tpr, thresholds = metrics.roc_curve(gt_list3, score_list3, pos_label=1) auc += metrics.auc(fpr, tpr)

print('auc = ', auc/140)`

Additionally, would it be possible to explain some of your idea for how to show the ROC curve?

zimengxueying commented 2 years ago

Hi, thanks for your amazing work!

I have been trying to plot the ROC curve for the performance evaluation, but I am confused about how the auc is calculated in the test.py. Could you kindly explain your method? For example, what does the number 140 mean in the equation auc/140 in the python file test.py?

`score_list3 = np.concatenate((score_list, score_list2), axis=0) gt_list3 = np.concatenate((gt_list, gt_list2), axis=0)

fpr, tpr, thresholds = metrics.roc_curve(gt_list3, score_list3, pos_label=1) auc += metrics.auc(fpr, tpr)

print('auc = ', auc/140)`

Additionally, would it be possible to explain some of your idea for how to show the ROC curve?