log_dir = FLAGS.log_dir class_names = get_classes(FLAGS.classes_file) num_classes = len(class_names) anchors = get_anchors(FLAGS.anchors_path) weights_path = FLAGS.weights_path input_shape = (416, 416) # multiple of 32, height, width epoch1, epoch2 = FLAGS.epochs, FLAGS.epochs is_tiny_version = (len(anchors) == 6) # default setting if FLAGS.is_tiny: model = create_tiny_model(input_shape, anchors, num_classes, freeze_body=2, weights_path=weights_path) else: model = create_model( input_shape, anchors, num_classes, freeze_body=2, weights_path=weights_path) # make sure you know what you freeze log_dir_time = os.path.join(log_dir, '{}'.format(int(time()))) logging = TensorBoard(log_dir=log_dir_time) checkpoint = ModelCheckpoint(os.path.join(log_dir, 'checkpoint.h5'), monitor='val_loss', save_weights_only=True,
weights_path = FLAGS.weights_path if FLAGS.is_tiny and FLAGS.anchors_path == anchors_path: anchors_path = os.path.join(os.path.dirname(FLAGS.anchors_path), "yolo-tiny_anchors.txt") else: anchors_path = FLAGS.anchors_path anchors = get_anchors(anchors_path) epoch1, epoch2 = FLAGS.epochs, FLAGS.epochs is_tiny_version = len(anchors) == 6 # default setting if FLAGS.is_tiny: model = create_tiny_model(INPUT_SHAPE, anchors, num_classes, freeze_body=2, weights_path=weights_path) else: model = create_model(INPUT_SHAPE, anchors, num_classes, freeze_body=2, weights_path=weights_path) log_dir_time = os.path.join(log_dir, "{}".format(int(time()))) logging = TensorBoard(log_dir=log_dir_time) checkpoint = ModelCheckpoint( os.path.join(log_dir, "checkpoint.h5"), monitor="val_loss", save_weights_only=True,