Comments (1)
You dont have to setup the number of classes. This code is the continuation of the code example presented in the main Mask-RCNN repo. You just compute the predictions for each images, assign each prediction (classe) to its actual ground-truth using two ordered vectors (gt_tot and pred_tot), and classically compute the confusion matrix for the entire dataset. These two vectors will contain in each element the ground-truth classe for each object in the dataset (the vector gt_tot) and the corresponding predicted classe (the vector pred_tot).
from confusion-matrix-for-mask-r-cnn.
Related Issues (14)
- Confusion matrix illustrate HOT 7
- Couldnt generate confusion matrix with 2 different classes (not included background)
- Confusion Matrix Illustration HOT 1
- The confusion matrix for yolact
- Explanation of Confusion Matrix Illustration
- Adding True Negatives
- Precision-Recall curve HOT 2
- Confusion matrix HOT 6
- Confusion Matrix HOT 11
- Getting only TPs HOT 12
- computation for entire dataset HOT 1
- false positives and false negatives seem mixed up HOT 3
- AttributeError: module 'mrcnn.utils' has no attribute 'gt_pred_lists' HOT 4
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from confusion-matrix-for-mask-r-cnn.