要按类别计算TensorFlow目标检测数量，可以使用以下解决方法：

1. 导入所需的库和模块：

import tensorflow as tf
from object_detection.utils import label_map_util
from collections import defaultdict

2. 加载训练好的模型和标签映射文件：

model_path = 'path_to_your_model_directory/frozen_inference_graph.pb'
label_map_path = 'path_to_your_label_map_file/label_map.pbtxt'

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.compat.v1.GraphDef()
    with tf.compat.v2.io.gfile.GFile(model_path, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

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)

3. 定义函数来执行目标检测：

def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.compat.v1.Session() as sess:
            # 获取输入和输出张量
            image_tensor = graph.get_tensor_by_name('image_tensor:0')
            detection_boxes = graph.get_tensor_by_name('detection_boxes:0')
            detection_scores = graph.get_tensor_by_name('detection_scores:0')
            detection_classes = graph.get_tensor_by_name('detection_classes:0')
            num_detections = graph.get_tensor_by_name('num_detections:0')

            # 运行推理
            (boxes, scores, classes, num) = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections],
                feed_dict={image_tensor: np.expand_dims(image, axis=0)})

            # 移除维度为1的尺寸
            boxes = np.squeeze(boxes)
            scores = np.squeeze(scores)
            classes = np.squeeze(classes).astype(np.int32)

            return boxes, scores, classes, num

4. 加载待检测的图像并执行目标检测：

image_path = 'path_to_your_image/image.jpg'
image = Image.open(image_path)
image_np = np.array(image)

boxes, scores, classes, num = run_inference_for_single_image(image_np, detection_graph)

5. 统计各个类别的数量：

count_dict = defaultdict(int)
for i in range(num):
    if scores[i] > 0.5:  # 可以调整置信度的阈值
        class_name = category_index[classes[i]]['name']
        count_dict[class_name] += 1

for class_name, count in count_dict.items():
    print(f'{class_name}: {count}')

这样就可以按类别计算TensorFlow目标检测数量并打印出结果。请确保替换代码中的路径为相应的模型、标签映射文件和图像的路径。