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,
