只是都大了点。另外,可以在分子分母同时加一个非常小的数,没有真值的就不会出现nan而是接近0的数。
MIoU(MeanIoU,MeanIntersection over Union,均交并比,交集 / 并集),也就是语义分割中所谓的 Mask IoU 。...y_true = y_true.flatten() current = confusion_matrix(y_true, y_pred, labels=[0, 1]) # computemean...ground_truth_set + predicted_set - intersection IoU = intersection / union.astype...
Your code ignores the left half of both frames, due to the wayignore_indexis used. This leads to an IoU value of 0.5, which is clearly wrong. In my example, 0.5 is the value of recall (sensitivity), which is what your code calculates. IoU = TP / (TP + FP + FN) recall = TP ...
MIoU,MeanIoU,MeanIntersection over Union,均交并比 MIoU(MeanIoU,MeanIntersection over Union,均交并比,交集 / 并集),也就是语义分割中所谓的 Mask IoU 。...y_true = y_true.flatten() current = confusion_matrix(y_true, y_pred, labels=[0, 1]) # computemean...ground_truth_set + predicted_set...
# 需要导入模块: import numpy [as 别名]# 或者: from numpy importnanmean[as 别名]defeval_detection_voc(pred_boxlists, gt_boxlists, iou_thresh=0.5, use_07_metric=False):"""Evaluate on voc dataset. Args: pred_boxlists(list[BoxList]): pred boxlist, has labels and scores fields. ...
MIoU,Mean IoU,Mean Intersection over Union,均交并比 MIoU(Mean IoU,Mean Intersection over Union,均交并比,交集 / 并集),也就是语义分割中所谓的 Mask IoU 。...y_true = y_true.flatten() current = confusion_matrix(y_true, y_pred, labels=[0, 1]) # compute mean...ground_truth_set + predi...
MIoU,Mean IoU,Mean Intersection over Union,均交并比 MIoU(Mean IoU,Mean Intersection over Union,均交并比,交集 / 并集),也就是语义分割中所谓的 Mask IoU 。...y_true = y_true.flatten() current = confusion_matrix(y_true, y_pred, labels=[0, 1]) # compute mean...ground_truth_set + predi...
MIoU,Mean IoU,Mean Intersection over Union,均交并比 MIoU(Mean IoU,Mean Intersection over Union,均交并比,交集 / 并集),也就是语义分割中所谓的 Mask IoU 。...y_true = y_true.flatten() current = confusion_matrix(y_true, y_pred, labels=[0, 1]) # compute mean...ground_truth_set + predi...
+ boxBArea - interArea) # return the intersection over union value return iou 后记 IoU在FCN中称为IU...,初看Fully Convolutional Networks for Semantic Segmentation论文,其中的IU概念没有能理解,其实那里的IU也就是IoU,检测物体轮廓不一定非得是方框...在实际的任务中,根据不同的任务要求来写不同具体实...
MIoU,MeanIoU,MeanIntersection over Union,均交并比 MIoU(MeanIoU,MeanIntersection over Union,均交并比,交集 / 并集),也就是语义分割中所谓的 Mask IoU 。...y_true = y_true.flatten() current = confusion_matrix(y_true, y_pred, labels=[0, 1]) # computemean...ground_truth_set + predicted_set...