def create_generators(args, group_queue):
# create image data generator objects
train_image_data_generator = keras.preprocessing.image.ImageDataGenerator(
horizontal_flip=True,
vertical_flip=True,
zoom_range=0.15,
rotation_range=25)
# train_image_data_generator = keras.preprocessing.image.ImageDataGenerator(
# horizontal_flip=True
# )
val_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from keras_retinanet.preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(args.coco_path,
'train2017',
train_image_data_generator,
batch_size=args.batch_size)
validation_generator = CocoGenerator(args.coco_path,
'val2017',
val_image_data_generator,
batch_size=args.batch_size)
elif args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(args.pascal_path,
'trainval',
train_image_data_generator,
batch_size=args.batch_size)
validation_generator = PascalVocGenerator(args.pascal_path,
'test',
val_image_data_generator,
batch_size=args.batch_size)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(args.annotations,
args.classes,
args.mean_image,
train_image_data_generator,
batch_size=args.batch_size,
image_min_side=int(args.image_min_side),
image_max_side=int(args.image_max_side),
group_queue=group_queue)
if args.val_annotations:
validation_generator = CSVGenerator(args.val_annotations,
args.classes,
args.mean_image,
val_image_data_generator,
batch_size=args.batch_size)
else:
validation_generator = None
else:
raise ValueError('Invalid data type received: {}'.format(dataset_type))
return train_generator, validation_generator
def create_generators(args):
# create image data generator objects
train_image_data_generator = keras.preprocessing.image.ImageDataGenerator(
horizontal_flip=True,
vertical_flip=True,
rotation_range=180,
width_shift_range=5,
height_shift_range=5,
preprocessing_function=add_noise # Kai - add noise to input
)
val_image_data_generator = keras.preprocessing.image.ImageDataGenerator(
# Kai - may want to add noise to input as well
)
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from keras_retinanet.preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(args.coco_path,
'train2017',
train_image_data_generator,
batch_size=args.batch_size)
validation_generator = CocoGenerator(args.coco_path,
'val2017',
val_image_data_generator,
batch_size=args.batch_size)
elif args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(args.pascal_path,
'trainval',
train_image_data_generator,
batch_size=args.batch_size)
validation_generator = PascalVocGenerator(args.pascal_path,
'test',
val_image_data_generator,
batch_size=args.batch_size)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(args.annotations,
args.classes,
train_image_data_generator,
batch_size=args.batch_size)
if args.val_annotations:
validation_generator = CSVGenerator(args.val_annotations,
args.classes,
val_image_data_generator,
batch_size=args.batch_size)
else:
validation_generator = None
else:
raise ValueError('Invalid data type received: {}'.format(dataset_type))
return train_generator, validation_generator
def create_generators(args):
# create image data generator objects
train_image_data_generator = keras.preprocessing.image.ImageDataGenerator(
horizontal_flip=True, )
val_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from keras_retinanet.preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(args.coco_path,
'train2017',
train_image_data_generator,
batch_size=args.batch_size)
validation_generator = CocoGenerator(args.coco_path,
'val2017',
val_image_data_generator,
batch_size=args.batch_size)
elif args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(args.pascal_path,
'trainval',
train_image_data_generator,
batch_size=args.batch_size)
validation_generator = PascalVocGenerator(args.pascal_path,
'test',
val_image_data_generator,
batch_size=args.batch_size)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(args.annotations,
args.classes,
train_image_data_generator,
batch_size=args.batch_size,
base_dir="./")
if args.val_annotations:
validation_generator = CSVGenerator(args.val_annotations,
args.classes,
val_image_data_generator,
batch_size=args.batch_size,
base_dir="./")
else:
validation_generator = None
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return train_generator, validation_generator
def create_generators(self, args, transform_generator=None):
train_generator = PascalVocGenerator(
args.pascal_path,
'trainval',
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = PascalVocGenerator(args.pascal_path,
'test',
batch_size=args.batch_size)
return {
"train_generator": train_generator,
"validation_generator": validation_generator
}
def create_generator(args, config):
""" Create generators for evaluation.
"""
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from keras_retinanet.preprocessing.coco import CocoGenerator
validation_generator = CocoGenerator(
args.coco_path,
'val2017',
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'pascal':
validation_generator = PascalVocGenerator(
args.pascal_path,
'test',
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'csv':
validation_generator = CSVGenerator(args.annotations,
args.classes,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'onthefly':
validation_generator = onthefly.OnTheFlyGenerator(
args.annotations,
batch_size=args.batch_size,
base_dir=config["rgb_tile_dir"],
config=config)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return validation_generator
def create_generator(args):
""" Create generators for evaluation.
"""
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from ..preprocessing.coco import CocoGenerator
validation_generator = CocoGenerator(
args.coco_path,
'val2017',
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config)
elif args.dataset_type == 'pascal':
validation_generator = PascalVocGenerator(
args.pascal_path,
'test',
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config)
elif args.dataset_type == 'csv':
validation_generator = CSVGenerator(args.annotations,
args.classes,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return validation_generator
def create_generator(args, preprocess_image):
""" Create generators for evaluation.
"""
common_args = {
'preprocess_image': preprocess_image,
}
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from keras_retinanet.preprocessing.coco import CocoGenerator
validation_generator = CocoGenerator(
args.coco_path,
'val2017',
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config,
shuffle_groups=False,
**common_args)
elif args.dataset_type == 'pascal':
validation_generator = PascalVocGenerator(
args.pascal_path,
'test',
image_extension=args.image_extension,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config,
shuffle_groups=False,
**common_args)
elif args.dataset_type == 'csv':
validation_generator = CSVGenerator(args.annotations,
args.classes,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config,
shuffle_groups=False,
**common_args)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return validation_generator
def create_generators(args, preprocess_image):
""" Create generators for training and validation.
Args
args : parseargs object containing configuration for generators.
preprocess_image : Function that preprocesses an image for the network.
"""
common_args = {
'batch_size' : args.batch_size,
'image_min_side' : args.image_min_side,
'image_max_side' : args.image_max_side,
'preprocess_image' : preprocess_image,
}
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_rotation=-0.05,
max_rotation=0.05,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
#min_shear=-0.1,
#max_shear=0.1,
min_scaling=(0.8, 0.8),
max_scaling=(1.2, 1.2),
flip_x_chance=0.5,
#flip_y_chance=0.5,
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from ..preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
'train2017',
transform_generator=transform_generator,
**common_args
)
validation_generator = CocoGenerator(
args.coco_path,
'val2017',
**common_args
)
elif args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(
args.pascal_path,
'trainval',
transform_generator=transform_generator,
**common_args
)
validation_generator = PascalVocGenerator(
args.pascal_path,
'test',
**common_args
)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(
args.annotations,
args.classes,
transform_generator=transform_generator,
**common_args
)
if args.val_annotations:
validation_generator = CSVGenerator(
args.val_annotations,
args.classes,
**common_args
)
else:
validation_generator = None
elif args.dataset_type == 'oid':
train_generator = OpenImagesGenerator(
args.main_dir,
subset='train',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
transform_generator=transform_generator,
**common_args
)
validation_generator = OpenImagesGenerator(
args.main_dir,
subset='validation',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
**common_args
)
elif args.dataset_type == 'kitti':
train_generator = KittiGenerator(
args.kitti_path,
subset='train',
transform_generator=transform_generator,
**common_args
)
validation_generator = KittiGenerator(
args.kitti_path,
subset='val',
**common_args
)
else:
raise ValueError('Invalid data type received: {}'.format(args.dataset_type))
return train_generator, validation_generator
args = parse_args()
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
# create the model
print('Loading model, this may take a second...')
model = keras.models.load_model(args.model, custom_objects=custom_objects)
# create image data generator object
test_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
# create a generator for testing data
# test_generator = CocoGenerator(
# args.coco_path,
# args.set,
# test_image_data_generator,
# )
test_generator = PascalVocGenerator(
args.voc_path,
'test',
test_image_data_generator,
)
evaluate_voc(test_generator, model, args.score_threshold)
model = keras.models.load_model(
'/data/users/xiziwang/tools/nsp/snapshots/origin_resnet50_voc_best.h5',
custom_objects=custom_objects)
#print(model.summary())
# ## Initialize data generators
# In[3]:
# create image data generator object
val_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
# create a generator for testing data
val_generator = PascalVocGenerator(
'/data/users/xiziwang/tools/nsp/JHdevkit/VOC2007',
'test',
val_image_data_generator,
batch_size=1,
)
index = 0
# ## Run detection on example
# In[6]:
# load image
f = open('/data/users/xiziwang/tobelabeled/files.txt', 'r')
files = f.read().splitlines()
result = open('/data/users/xiziwang/tobelabeled/result.txt', 'w')
for fn in files:
if __name__ == '__main__':
# parse arguments
args = parse_args()
train_path = args.path
# create image data generator objects
train_image_data_generator = keras.preprocessing.image.ImageDataGenerator(
horizontal_flip=False, )
# create a generator for training data
train_generator = PascalVocGenerator(
data_dir=train_path,
set_name='train',
image_data_generator=train_image_data_generator,
)
test_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
# create a generator for testing data
test_generator = PascalVocGenerator(
data_dir=train_path,
set_name='val',
image_data_generator=test_image_data_generator,
)
num_classes = train_generator.num_classes()
# create the model
def create_generator(args, config):
""" Create the data generators.
Args:
args: parseargs arguments object.
"""
# create random transform generator for augmenting training data
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from ..preprocessing.coco import CocoGenerator
generator = CocoGenerator(args.coco_path,
args.coco_set,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'pascal':
generator = PascalVocGenerator(args.pascal_path,
args.pascal_set,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'csv':
generator = CSVGenerator(args.annotations,
args.classes,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'oid':
generator = OpenImagesGenerator(
args.main_dir,
subset=args.subset,
version=args.version,
labels_filter=args.labels_filter,
fixed_labels=args.fixed_labels,
annotation_cache_dir=args.annotation_cache_dir,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'kitti':
generator = KittiGenerator(args.kitti_path,
subset=args.subset,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'onthefly':
validation_generator = onthefly.OnTheFlyGenerator(
args.annotations, batch_size=args.batch_size, config=config)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return generator
def create_generators(args):
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_translation=(-0.3, -0.3),
max_translation=(0.3, 0.3),
min_scaling=(0.2, 0.2),
max_scaling=(2, 2),
flip_x_chance=0.5,
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from ..preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
'train2017',
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = CocoGenerator(args.coco_path,
'val2017',
batch_size=args.batch_size)
elif args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(
args.pascal_path,
'trainval',
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = PascalVocGenerator(args.pascal_path,
'test',
batch_size=args.batch_size)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(args.annotations,
args.classes,
transform_generator=transform_generator,
batch_size=args.batch_size)
if args.val_annotations:
validation_generator = CSVGenerator(args.val_annotations,
args.classes,
batch_size=args.batch_size)
else:
validation_generator = None
elif args.dataset_type == 'oid':
train_generator = OpenImagesGenerator(
args.main_dir,
subset='train',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
fixed_labels=args.fixed_labels,
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = OpenImagesGenerator(
args.main_dir,
subset='validation',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
fixed_labels=args.fixed_labels,
batch_size=args.batch_size)
elif args.dataset_type == 'kitti':
train_generator = KittiGenerator(
args.kitti_path,
subset='train',
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = KittiGenerator(args.kitti_path,
subset='val',
batch_size=args.batch_size)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return train_generator, validation_generator
def get_data_generator(args):
backbone = models.backbone(args.backbone)
preprocess_image = backbone.preprocess_image
common_args = {
'batch_size': args.batch_size,
'config': args.config,
'image_min_side': args.image_min_side,
'image_max_side': args.image_max_side,
'preprocess_image': preprocess_image,
}
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
visual_effect_generator = random_visual_effect_generator(
contrast_range=(0.9, 1.1),
brightness_range=(-.1, .1),
hue_range=(-0.05, 0.05),
saturation_range=(0.95, 1.05))
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
visual_effect_generator = None
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from ..preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
'train2017',
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
**common_args)
validation_generator = CocoGenerator(args.coco_path,
'val2017',
shuffle_groups=False,
**common_args)
elif args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(
args.pascal_path,
'trainval',
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
**common_args)
validation_generator = PascalVocGenerator(args.pascal_path,
'test',
shuffle_groups=False,
**common_args)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(
args.annotations,
args.classes,
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
**common_args)
if args.val_annotations:
validation_generator = CSVGenerator(args.val_annotations,
args.classes,
shuffle_groups=False,
**common_args)
else:
validation_generator = None
elif args.dataset_type == 'oid':
train_generator = OpenImagesGenerator(
args.main_dir,
subset='train',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
**common_args)
validation_generator = OpenImagesGenerator(
args.main_dir,
subset='validation',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
shuffle_groups=False,
**common_args)
elif args.dataset_type == 'kitti':
train_generator = KittiGenerator(
args.kitti_path,
subset='train',
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
**common_args)
validation_generator = KittiGenerator(args.kitti_path,
subset='val',
shuffle_groups=False,
**common_args)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return train_generator, validation_generator
def create_generators(args, preprocess_image):
""" Create generators for training and validation.
Args
args : parseargs object containing configuration for generators.
preprocess_image : Function that preprocesses an image for the network.
"""
common_args = {
"batch_size": args.batch_size,
"config": args.config,
"image_min_side": args.image_min_side,
"image_max_side": args.image_max_side,
"preprocess_image": preprocess_image,
}
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
if args.dataset_type == "coco":
# import here to prevent unnecessary dependency on cocoapi
from keras_retinanet.preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
"train2017",
transform_generator=transform_generator,
**common_args)
validation_generator = CocoGenerator(args.coco_path,
"val2017",
shuffle_groups=False,
**common_args)
elif args.dataset_type == "pascal":
train_generator = PascalVocGenerator(
args.pascal_path,
"trainval",
transform_generator=transform_generator,
**common_args)
validation_generator = PascalVocGenerator(args.pascal_path,
"test",
shuffle_groups=False,
**common_args)
elif args.dataset_type == "csv":
train_generator = CSVGenerator(args.annotations,
args.classes,
transform_generator=transform_generator,
**common_args)
if args.val_annotations:
validation_generator = CSVGenerator(args.val_annotations,
args.classes,
shuffle_groups=False,
**common_args)
else:
validation_generator = None
elif args.dataset_type == "oid":
train_generator = OpenImagesGenerator(
args.main_dir,
subset="train",
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
transform_generator=transform_generator,
**common_args)
validation_generator = OpenImagesGenerator(
args.main_dir,
subset="validation",
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
shuffle_groups=False,
**common_args)
elif args.dataset_type == "kitti":
train_generator = KittiGenerator(
args.kitti_path,
subset="train",
transform_generator=transform_generator,
**common_args)
validation_generator = KittiGenerator(args.kitti_path,
subset="val",
shuffle_groups=False,
**common_args)
else:
raise ValueError("Invalid data type received: {}".format(
args.dataset_type))
return train_generator, validation_generator
'regression': keras_retinanet.losses.smooth_l1(),
'classification': keras_retinanet.losses.focal()
},
optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001))
# print model summary
print(model.summary())
# create image data generator objects
train_image_data_generator = keras.preprocessing.image.ImageDataGenerator(
horizontal_flip=True, )
val_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
# create a generator for training data
train_generator = PascalVocGenerator(args.voc_path,
'trainval',
train_image_data_generator,
batch_size=args.batch_size)
# create a generator for testing data
val_generator = PascalVocGenerator(args.voc_path,
'test',
val_image_data_generator,
batch_size=args.batch_size)
# start training
model.fit_generator(
generator=train_generator,
steps_per_epoch=len(train_generator.image_names) // args.batch_size,
epochs=50,
verbose=1,
validation_data=val_generator,
# parse arguments
args = parse_args()
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
# create the model
print('Loading model, this may take a second...')
model = keras.models.load_model(args.model, custom_objects=custom_objects)
# create image data generator object
test_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
# create a generator for testing data
test_generator = PascalVocGenerator(args.pascal_path, args.set,
test_image_data_generator)
voc_evaluator = VOCEvaluator(generator=test_generator,
model=model,
threshold=args.score_threshold,
iou_threshold=args.iou_threshold,
max_detections=args.max_det,
save=args.save_images,
save_path=args.save_path)
voc_evaluator.evaluate()