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,
save_best_only=True,
period=5,
)
reduce_lr = ReduceLROnPlateau(monitor="val_loss",
factor=0.1,
patience=3,
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,
save_best_only=True,
period=5)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=3,
verbose=1)
early_stopping = EarlyStopping(monitor='val_loss',
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',
factor=0.98,
patience=3,
verbose=1)
val_split = FLAGS.val_split
with open(FLAGS.annotation_file) as f:
lines = f.readlines()
