chnaged augmentation to augment

docs/en/modes/train.md

@@ -225,7 +225,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
 | `dropout`         | `0.0`    | Dropout rate for regularization in classification tasks, preventing overfitting by randomly omitting units during training.                                                                                          |
 | `val`             | `True`   | Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset.                                                                                                     |
 | `plots`           | `False`  | Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression.                                             |
-| `augmentation`    | `True`   | Enables data augmentation during training, introducing variability into the training data to improve model generalization.                                                                                           |
+| `augment`         | `True`   | Enables data augmentation during training, introducing variability into the training data to improve model generalization.                                                                                           |
 
 !!! info "Note on Batch-size Settings"
 

ultralytics/cfg/default.yaml

@@ -35,7 +35,6 @@ fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images
 profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
 freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
 multi_scale: False # (bool) Whether to use multiscale during training
-augmentation: True # (bool) Enables data augmentation during training
 # Segmentation
 overlap_mask: True # (bool) masks should overlap during training (segment train only)
 mask_ratio: 4 # (int) mask downsample ratio (segment train only)
@@ -59,7 +58,7 @@ source: # (str, optional) source directory for images or videos
 vid_stride: 1 # (int) video frame-rate stride
 stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
 visualize: False # (bool) visualize model features
-augment: False # (bool) apply image augmentation to prediction sources
+augment: False # (bool) apply image augmentation to prediction and training sources
 agnostic_nms: False # (bool) class-agnostic NMS
 classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
 retina_masks: False # (bool) use high-resolution segmentation masks

ultralytics/data/build.py

@@ -88,7 +88,7 @@ def build_yolo_dataset(cfg, img_path, batch, data, mode="train", rect=False, str
         img_path=img_path,
         imgsz=cfg.imgsz,
         batch_size=batch,
-        augment=cfg.augmentation if mode == "train" else False,  # augmentation
+        augment=cfg.augment if mode == "train" else False,  # augmentation
         hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
         rect=cfg.rect or rect,  # rectangular batches
         cache=cfg.cache or None,
@@ -110,7 +110,7 @@ def build_grounding(cfg, img_path, json_file, batch, mode="train", rect=False, s
         json_file=json_file,
         imgsz=cfg.imgsz,
         batch_size=batch,
-        augment=cfg.augmentation if mode == "train" else False,  # augmentation
+        augment=cfg.augment if mode == "train" else False,  # augmentation
         hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
         rect=cfg.rect or rect,  # rectangular batches
         cache=cfg.cache or None,