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Я запускаю Tensorflow Lite на своем Raspberry Pi 3b с пользовательским режимом обнаружения объектов. Я протестировал его на наборе данных Google COCO, и он прекрасно работает, но когда я тестирую его на своей пользовательской обученной модели, он не работает, несмотря на то, что модель прошла оценку производителя моделей TfLite. Когда я запускаю его, единственная ошибка, которую я получаю в своем сообщении, — это «Ошибка сегментации». Как я могу это исправить?

Я не могу загрузить свою модель в Stackoverflow, но только некоторую информацию об этом. Он обнаруживает только один объект, он не квантован, он обучен на основе режима efficientdet_lite1, и я обучил его с помощью официального производителя моделей Tensorflow Lite Google Colab.

Вот код, используемый для интерпретации модели на моем Pi.

 ed=True) parser.add_argument('--graph', help='Name of the .tflite file, if different than detect.tflite',  default='detect.tflite') parser.add_argument('--labels', help='Name of the labelmap file, if different than labelmap.txt',  default='labelmap.txt') parser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects',  default=0.5) parser.add_argument('--resolution', help='Desired webcam resolution in WxH. If the webcam does not support the resolution entered, errors may occur.',  default='1280x720') parser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection',  action='store_true')  args = parser.parse_args()  MODEL_NAME = args.modeldir GRAPH_NAME = args.graph LABELMAP_NAME = args.labels min_conf_threshold = float(args.threshold) resW, resH = args.resolution.split('x') imW, imH = int(resW), int(resH) use_TPU = args.edgetpu  # Import TensorFlow libraries # If tflite_runtime is installed, import interpreter from tflite_runtime, else import from regular tensorflow # If using Coral Edge TPU, import the load_delegate library pkg = importlib.util.find_spec('tflite_runtime') if pkg:  from tflite_runtime.interpreter import Interpreter  if use_TPU:  from tflite_runtime.interpreter import load_delegate else:  from tensorflow.lite.python.interpreter import Interpreter  if use_TPU:  from tensorflow.lite.python.interpreter import load_delegate  # If using Edge TPU, assign filename for Edge TPU model if use_TPU:  # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'  if (GRAPH_NAME == 'detect.tflite'):  GRAPH_NAME = 'edgetpu.tflite'   # Get path to current working directory CWD_PATH = os.getcwd()  # Path to .tflite file, which contains the model that is used for object detection PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)  # Path to label map file PATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)  # Load the label map with open(PATH_TO_LABELS, 'r') as f:  labels = [line.strip() for line in f.readlines()]  # Have to do a weird fix for label map if using the COCO "starter model" from # https://www.tensorflow.org/lite/models/object_detection/overview # First label is '???', which has to be removed. if labels[0] == '???':  del(labels[0])  # Load the Tensorflow Lite model. # If using Edge TPU, use special load_delegate argument if use_TPU:  interpreter = Interpreter(model_path=PATH_TO_CKPT,  experimental_delegates=[load_delegate('libedgetpu.so.1.0')])  print(PATH_TO_CKPT) else:  interpreter = Interpreter(model_path=PATH_TO_CKPT)  interpreter.allocate_tensors()  # Get model details input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() height = input_details[0]['shape'][1] width = input_details[0]['shape'][2]  floating_model = (input_details[0]['dtype'] == np.float32)  input_mean = 127.5 input_std = 127.5  # Initialize frame rate calculation frame_rate_calc = 1 freq = cv2.getTickFrequency()  # Initialize video stream videostream = VideoStream(resolution=(imW,imH),framerate=30).start() time.sleep(1)  #for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True): while True:   # Start timer (for calculating frame rate)  t1 = cv2.getTickCount()   # Grab frame from video stream  frame1 = videostream.read()   # Acquire frame and resize to expected shape [1xHxWx3]  frame = frame1.copy()  frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  frame_resized = cv2.resize(frame_rgb, (width, height))  input_data = np.expand_dims(frame_resized, axis=0)   # Normalize pixel values if using a floating model (i.e. if model is non-quantized)  if floating_model:  input_data = (np.float32(input_data) - input_mean) / input_std   # Perform the actual detection by running the model with the image as input  interpreter.set_tensor(input_details[0]['index'],input_data)  interpreter.invoke()   # Retrieve detection results  boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects  classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects  scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects  #num = interpreter.get_tensor(output_details[3]['index'])[0] # Total number of detected objects (inaccurate and not needed)   # Loop over all detections and draw detection box if confidence is above minimum threshold  for i in range(len(scores)):  if ((scores[i] gt; min_conf_threshold) and (scores[i] lt;= 1.0)):   # Get bounding box coordinates and draw box  # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()  ymin = int(max(1,(boxes[i][0] * imH)))  xmin = int(max(1,(boxes[i][1] * imW)))  ymax = int(min(imH,(boxes[i][2] * imH)))  xmax = int(min(imW,(boxes[i][3] * imW)))    cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)   # Draw label  object_name = labels[int(classes[i])] # Look up object name from "labels" array using class index  label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'  labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size  label_ymin = max(ymin, labelSize[1]   10) # Make sure not to draw label too close to top of window  cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin labelSize[0], label_ymin baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in  cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text   # Draw framerate in corner of frame  cv2.putText(frame,'FPS: {0:.2f}'.format(frame_rate_calc),(30,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA)   # All the results have been drawn on the frame, so it's time to display it.  cv2.imshow('Object detector', frame)   # Calculate framerate  t2 = cv2.getTickCount()  time1 = (t2-t1)/freq  frame_rate_calc= 1/time1   # Press 'q' to quit  if cv2.waitKey(1) == ord('q'):  break  # Clean up cv2.destroyAllWindows() videostream.stop()