Tensorflow: google colab上的对象检测api错误-没有名为lvis的模块

# 安装lvis库
!pip install lvis

# 检查TensorFlow版本
import tensorflow as tf
print(tf.__version__)

# 导入必要的模块
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.builders import model_builder
from object_detection.utils import config_util

# 加载配置文件
config_path = 'path/to/your/config'
configs = config_util.get_configs_from_pipeline_file(config_path)
model_config = configs['model']
detection_model = model_builder.build(model_config=model_config, is_training=False)

# 加载权重文件
ckpt_path = 'path/to/your/checkpoint'
detection_model.load_weights(ckpt_path)

# 加载标签映射
label_map_path = 'path/to/your/label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(label_map_path, use_display_name=True)

# 进行对象检测
image_np = cv2.imread('path/to/your/image.jpg')
input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
detections = detection_model(input_tensor)

# 可视化检测结果
viz_utils.visualize_boxes_and_labels_on_image_array(
    image_np,
    detections['detection_boxes'][0].numpy(),
    detections['detection_classes'][0].numpy().astype(np.int64),
    detections['detection_scores'][0].numpy(),
    category_index,
    use_normalized_coordinates=True,
    max_boxes_to_draw=200,
    min_score_thresh=.30,
    agnostic_mode=False)

# 显示图像
cv2.imshow('object detection', cv2.resize(image_np, (800, 600)))
cv2.waitKey(0)
cv2.destroyAllWindows()