(), f"negative label values {lb[lb < 0]}" # 判断标签中是否有重复的坐标 assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}" _, i = np.unique(lb, axis=0, return_index=True) if len(i) < nl: # duplicate ...
assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}' assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of boundscoordinates{lb[:, 1:][lb[:, 1:] > 1]}' _, i = np.unique(lb, axis=0, return_index=True) #去掉重复标签信息 if len(i) < nl: # d...
shape[1] == 5, '> 5 label columns: %s' % file # 保证标签数据都大于0,即class>0,x>0,y>0,w>0,h>0 assert (l >= 0).all(), 'negative labels: %s' % file # 保证x,y,w,h都归一化为0~1 assert (l[:, 1:] <= 1).all()...
P(Positive):模型预测其是正例(目标本身是个鱼,模型也预测它是个鱼)。 N(Negative):模型预测其是负例(目标本身是个鱼,但模型预测它是个猫)。 TP:样本的真实类别是正例,并且模型预测的结果也是正例,预测正确(目标本身是个鱼,模型也预测它是鱼,预测正确;还有一种理解方式,模型预测它是正例,最终预测结果是正...
train: WARNING: /content/yolov5/train_data/images/train/00003.jpg: ignoring corrupt image/label: negative label values [ -1] train: WARNING: /content/yolov5/train_data/images/train/00004.jpg: ignoring corrupt image/label: negative label values [ -1] ...
df.text_sentiment)### 0:Neutral 1:Negative 2:Positive### 将情感标签映射为【0,1,2】label=pd...
④.假阴性(FalseNegative,FN)样本的真实类别是正例,但是模型将其预测成为负例,预测错误 ⼏个指标 1)正确率(accuracy) 正确率是我们最常见的评价指标,accuracy=(TP+TN)/(P+N),这个很容易理解,就是被分对的样本数除以所有的样本数,通常来 说,正确率越⾼,分类器越好; ...
precision (mAP), a measure more adequate in the evaluation of detection performance in 3D tracking is proposed, namely the average distance between the centroids of matched references and detected drones, including false positive and false negative ratios. Moreover, the videos generated in the ...
IoU values for every element in boxes1 and boxes2 注: N表示预测框数,M表示真实框数,计算预测框与真实框IoU值 """ # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) ...
all(), f'negative label values {l[l < 0]}' assert (l[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {l[:, 1:][l[:, 1:] > 1]}' _, i = np.unique(l, axis=0, return_index=True) assert lb.shape[1] == 5, f'labels require 5 columns, {lb....