Update articles/machine-learning/how-to-auto-train-image-models.md

Co-authored-by: Rupal jain <[email protected]>

Author: nvijayrania

articles/machine-learning/how-to-auto-train-image-models.md

@@ -533,7 +533,7 @@ In general, deep learning model performance can often improve with more data. Da
 |Object detection, instance segmentation| Training <br><br> Validation & Test |Random crop around bounding boxes, expand, horizontal flip, normalization, resize <br><br><br>Normalization, resize
 |Object detection using yolov5| Training <br><br> Validation & Test  |Mosaic, random affine (rotation, translation, scale, shear), horizontal flip <br><br><br> Letterbox resizing|
 
-Currently the augmentations defined above are applied by default for an Automated ML job. To provide control over augmentations, Automated ML expose flags to turn-off certain augmentations. Currently, these flags are only supported for object detection and instance segmentation tasks. We expose following two flags to turn-off the augmentations.
+Currently the augmentations defined above are applied by default for an Automated ML for image job. To provide control over augmentations, Automated ML for images exposes below two flags to turn-off certain augmentations. Currently, these flags are only supported for object detection and instance segmentation tasks. 
  1. **apply_mosaic_for_yolo:** This flag is only specific to Yolo model. Setting it to False turns off the mosaic data augmentation which is applied at the training time.
  2. **apply_automl_train_augmentations:** Setting this flag to false turns off the augmentation applied during training time for the object detection and instance segmentation models. For augmentations, see the details in the table above. 
  - For non-yolo object detection model and instance segmentation models, this flag turns off only the first three augmentations i.e., *Random crop around bounding boxes, expand, horizontal flip*. The normalization and resize augmentations are still applied regardless of this flag.