diff --git a/keras_retinanet/bin/evaluate_coco.py b/keras_retinanet/bin/evaluate_coco.py index e29db7a52..374c06eb4 100755 --- a/keras_retinanet/bin/evaluate_coco.py +++ b/keras_retinanet/bin/evaluate_coco.py @@ -75,9 +75,7 @@ def main(args=None): # create a generator for testing data test_generator = CocoGenerator( args.coco_path, - args.set, - image_min_side=800, - image_max_side=1333, + args.set ) evaluate_coco(test_generator, model, args.score_threshold) diff --git a/keras_retinanet/bin/train.py b/keras_retinanet/bin/train.py index 03d3949af..1033793d0 100755 --- a/keras_retinanet/bin/train.py +++ b/keras_retinanet/bin/train.py @@ -158,17 +158,13 @@ def create_generators(args): args.coco_path, 'train2017', transform_generator=transform_generator, - batch_size=args.batch_size, - image_min_side=800, - image_max_side=1333, + batch_size=args.batch_size ) validation_generator = CocoGenerator( args.coco_path, 'val2017', - batch_size=args.batch_size, - image_min_side=800, - image_max_side=1333, + batch_size=args.batch_size ) elif args.dataset_type == 'pascal': train_generator = PascalVocGenerator( diff --git a/keras_retinanet/preprocessing/generator.py b/keras_retinanet/preprocessing/generator.py index 6916e53d0..ed37ddf25 100644 --- a/keras_retinanet/preprocessing/generator.py +++ b/keras_retinanet/preprocessing/generator.py @@ -40,8 +40,8 @@ def __init__( batch_size=1, group_method='ratio', # one of 'none', 'random', 'ratio' shuffle_groups=True, - image_min_side=600, - image_max_side=1024, + image_min_side=800, + image_max_side=1333, transform_parameters=None, ): self.transform_generator = transform_generator diff --git a/keras_retinanet/utils/image.py b/keras_retinanet/utils/image.py index a50490334..847a97ade 100644 --- a/keras_retinanet/utils/image.py +++ b/keras_retinanet/utils/image.py @@ -160,7 +160,7 @@ def apply_transform(matrix, image, params): return output -def resize_image(img, min_side=600, max_side=1024): +def resize_image(img, min_side=800, max_side=1333): (rows, cols, _) = img.shape smallest_side = min(rows, cols)