) parser.add_argument( "--epochs", type=float, default=51, help= "Number of epochs for training last layers and number of epochs for fine-tuning layers. Default is 51." ) FLAGS = parser.parse_args() np.random.seed(FLAGS.random_seed) 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:
default=300, help= "Number of epochs for training last layers and number of epochs for fine-tuning layers. Default is 51." ) parser.add_argument( "--pre_trained_path", type=str, dest='pre_trained_weights_path', default=None, help="Absolute path for pre trained weights. default is None.") FLAGS = parser.parse_args() np.random.seed(None) weights_folder = FLAGS.weights_folder_path class_names = get_classes(classname_file) num_classes = len(class_names) anchors = get_anchors(anchors_path) input_shape = (416, 416) epoch1, epoch2 = FLAGS.epochs, FLAGS.epochs model = create_model(input_shape, anchors, num_classes, freeze_body=2, weights_path=weights_path) if FLAGS.pre_trained_weights_path != None: print('load pre trained weights: ' + FLAGS.pre_trained_weights_path) model.load_weights(FLAGS.pre_trained_weights_path) reduce_lr = ReduceLROnPlateau(monitor='val_loss',