问题描述
在Tensorflow对象检测API中，我们通常针对每个测试图像执行此操作:
in Tensorflow object detection API, we usually do this for each test image:
output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image_np_expanded})
# pdb.set_trace()
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.int64)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
这为我们提供了每个对象的分数.例如，有一个狗物体，它的分数是95％.但是，我希望它也输出其他类别的分数，例如，对于同一个对象，分数为猫的分数为3％，而自行车的分数为2％.请帮助我，非常感谢.
This gives us the score for each object. For example, there is a dog object, and the score for it is 95%. However I want it to output the score for other classes as well, for example, with the same object, the score for it being a cat would be 3%, being a bicycle would be 2%. Please help me, thank you very much.
推荐答案
事实证明，在较新版本的API中，有output_dict ['detection_multiclass_scores']给出了每个类的分数.我使用了一个没有的旧版本.
It turns out in newer versions of the API, there was output_dict['detection_multiclass_scores'] which gives scores for each class. I used an older version which did not have it.
这篇关于使用Tensorflow对象检测API的每个对象的所有类的输出得分/概率的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！