如何在Tensorflow对象检测API中获取预测值的百分比？

import tensorflow as tf
from object_detection.utils import label_map_util

# 加载标签映射文件
label_map_path = 'path/to/label_map.pbtxt'
label_map = label_map_util.load_labelmap(label_map_path)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

# 加载模型
model_path = 'path/to/model'
detection_model = tf.saved_model.load(model_path)

# 加载待检测的图像
image_path = 'path/to/image.jpg'
image_np = tf.image.decode_image(open(image_path, 'rb').read(), channels=3)

# 图像预处理
input_tensor = tf.convert_to_tensor(image_np)
input_tensor = input_tensor[tf.newaxis, ...]

# 目标检测
detections = detection_model(input_tensor)

# 获取预测值的百分比
for i in range(detections['detection_scores'][0].numpy().shape[0]):
    class_id = int(detections['detection_classes'][0][i].numpy())
    class_name = category_index[class_id]['name']
    score = detections['detection_scores'][0][i].numpy()
    percentage = round(score * 100, 2)
    print(f"Detected {class_name} with {percentage}% confidence.")