Tensorflow 对象检测 API 中的 Precision-Recall 曲线 [英] Precision-Recall Curve in Tensorflow Object Detection API

问题描述

有没有办法在 Tensorflow 对象检测 API 中可视化 Precision-Recall 曲线?我知道 mAP 代表曲线下面积的绝对值，但我认为实际曲线更能代表我的应用程序?我已经在 utils/metrics1 中找到了一些精度和召回值，但我不知道它们究竟代表什么，或者更好地说，如何从中生成 Prec/Recall 曲线.有人可以帮我吗，怎么做?

Is there a way to visualize the Precision-Recall Curve in the Tensorflow Object Detection API? I know that the mAP represents absolute value of the area under the curve, but i think the actual curve would be more representable for my application? I already found some precision and recall values within the utils/metrics1, but I don't know what they represent exactly, or better said, how to generate a Prec/Recall Curve out of them. Can somebody help me, how to do that?

推荐答案

compute_precision_recall 输出两个相同长度的 numpy 数组，即.准确率和召回率.您可以使用 matplotlib 轻松绘制这些值:

compute_precision_recall outputs two numpy arrays of the same length, ie. precision and recall. You can easily plot these values with matplotlib:

import matplotlib.pyplot as plt
...
precision, recall = compute_precision_recall(scores, labels, num_gt)
plt.figure()
plt.step(recall, precision, where='post' )
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.xlim((0, 1))
plt.ylim((0, 1))
plt.show()
plt.close()

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