如何在this论文中计算平均IU (平均交叉联盟)得分?
Long,Jonathan,Evan Shelhamer和Trevor Darrell。 "用于语义分割的完全卷积网络。"
答案 0 :(得分:23)
对于每个班级联盟的交叉(IU)得分为:
真阳性/(真阳性+假阳性+假阴性)
意味着IU 只是所有类别的平均值。
关于文件中的符号:
n_ij:预计属于 j 类的 i 类的像素数。所以对于班级 i :
您可以在Pascak DevKit中找到直接计算的matlab代码here
答案 1 :(得分:9)
from sklearn.metrics import confusion_matrix
import numpy as np
def compute_iou(y_pred, y_true):
# ytrue, ypred is a flatten vector
y_pred = y_pred.flatten()
y_true = y_true.flatten()
current = confusion_matrix(y_true, y_pred, labels=[0, 1])
# compute mean iou
intersection = np.diag(current)
ground_truth_set = current.sum(axis=1)
predicted_set = current.sum(axis=0)
union = ground_truth_set + predicted_set - intersection
IoU = intersection / union.astype(np.float32)
return np.mean(IoU)
答案 2 :(得分:2)
这应该有帮助
def computeIoU(y_pred_batch, y_true_batch):
return np.mean(np.asarray([pixelAccuracy(y_pred_batch[i], y_true_batch[i]) for i in range(len(y_true_batch))]))
def pixelAccuracy(y_pred, y_true):
y_pred = np.argmax(np.reshape(y_pred,[N_CLASSES_PASCAL,img_rows,img_cols]),axis=0)
y_true = np.argmax(np.reshape(y_true,[N_CLASSES_PASCAL,img_rows,img_cols]),axis=0)
y_pred = y_pred * (y_true>0)
return 1.0 * np.sum((y_pred==y_true)*(y_true>0)) / np.sum(y_true>0)
答案 3 :(得分:0)
jaccard_similarity_score
(每个How to find IoU from segmentation masks?)可用于获得与上面@ Alex-zhai的代码相同的结果:
import numpy as np
from sklearn.metrics import jaccard_score
y_true = np.array([[0, 1, 1],
[1, 1, 0]])
y_pred = np.array([[1, 1, 1],
[1, 0, 0]])
labels = [0, 1]
jaccards = []
for label in labels:
jaccard = jaccard_score(y_pred.flatten(),y_true.flatten(), pos_label=label)
jaccards.append(jaccard)
print(f'avg={np.mean(jaccards)}')