Remove unused code (#4327)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/data/augment.py

@@ -17,8 +17,6 @@ from ultralytics.utils.ops import segment2box
 
 from .utils import polygons2masks, polygons2masks_overlap
 
-POSE_FLIPLR_INDEX = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
-
 
 # TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic
 class BaseTransform:

ultralytics/data/dataset.py

@@ -30,7 +30,6 @@ class YOLODataset(BaseDataset):
         (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model.
     """
     cache_version = '1.0.2'  # dataset labels *.cache version, >= 1.0.0 for YOLOv8
-    rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
 
     def __init__(self, *args, data=None, use_segments=False, use_keypoints=False, **kwargs):
         self.use_segments = use_segments

ultralytics/data/utils.py

@@ -28,8 +28,6 @@ HELP_URL = 'See https://docs.ultralytics.com/datasets/detect for dataset formatt
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # image suffixes
 VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv', 'webm'  # video suffixes
 PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders
-IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
-IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation
 
 # Get orientation exif tag
 for orientation in ExifTags.TAGS.keys():