)
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',
