Merge pull request #188383 from sharma-riti/risha/update_nms_thresh

rename box_nms_thresh to nms_iou_thresh

Author: PRMerger20

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

@@ -493,8 +493,8 @@ Each of the tasks (and some models) have a set of parameters in the `model_setti
 | Task | Parameter name | Default  |
 |--------- |------------- | --------- |
 |Image classification (multi-class and multi-label) | `valid_resize_size`<br>`valid_crop_size` | 256<br>224 |
-|Object detection, instance segmentation| `min_size`<br>`max_size`<br>`box_score_thresh`<br>`box_nms_thresh`<br>`box_detections_per_img` | 600<br>1333<br>0.3<br>0.5<br>100 |
-|Object detection using `yolov5`| `img_size`<br>`model_size`<br>`box_score_thresh`<br>`box_iou_thresh` | 640<br>medium<br>0.1<br>0.5 |
+|Object detection, instance segmentation| `min_size`<br>`max_size`<br>`box_score_thresh`<br>`nms_iou_thresh`<br>`box_detections_per_img` | 600<br>1333<br>0.3<br>0.5<br>100 |
+|Object detection using `yolov5`| `img_size`<br>`model_size`<br>`box_score_thresh`<br>`nms_iou_thresh` | 640<br>medium<br>0.1<br>0.5 |
 
 For a detailed description on task specific hyperparameters, please refer to [Hyperparameters for computer vision tasks in automated machine learning](reference-automl-images-hyperparameters.md).
 

articles/machine-learning/reference-automl-images-hyperparameters.md

@@ -30,8 +30,7 @@ This table summarizes hyperparameters specific to the `yolov5` algorithm.
 | `model_size` | Model size. <br> Must be `small`, `medium`, `large`, or `xlarge`. <br><br> *Note: training run may get into CUDA OOM if the model size is too big*.  | `medium` |
 | `multi_scale` | Enable multi-scale image by varying image size by +/- 50% <br> Must be 0 or 1. <br> <br> *Note: training run may get into CUDA OOM if no sufficient GPU memory*. | 0 |
 | `box_score_thresh` | During inference, only return proposals with a score greater than `box_score_thresh`. The score is the multiplication of the objectness score and classification probability. <br> Must be a float in the range [0, 1]. | 0.1 |
-| `nms_iou_thresh` | IoU threshold used during inference in non-maximum suppression post processing. <br> Must be a float in the range [0, 1]. | 0.5 |
-
+| `nms_iou_thresh` | IOU threshold used during inference in non-maximum suppression post processing. <br> Must be a float in the range [0, 1]. | 0.5 |
 
 ## Model agnostic hyperparameters
 
@@ -92,7 +91,7 @@ The following hyperparameters are for object detection and instance segmentation
 | `min_size` | Minimum size of the image to be rescaled before feeding it to the backbone. <br> Must be a positive integer. <br> <br> *Note: training run may get into CUDA OOM if the size is too big*.| 600 |
 | `max_size` | Maximum size of the image to be rescaled before feeding it to the backbone. <br> Must be a positive integer.<br> <br> *Note: training run may get into CUDA OOM if the size is too big*. | 1333 |
 | `box_score_thresh` | During inference, only return proposals with a classification score greater than `box_score_thresh`. <br> Must be a float in the range [0, 1].| 0.3 |
-| `box_nms_thresh` | Non-maximum suppression (NMS) threshold for the prediction head. Used during inference.  <br>Must be a float in the range [0, 1]. | 0.5 |
+| `nms_iou_thresh` | IOU (intersection over union) threshold used in non-maximum suppression (NMS) for the prediction head. Used during inference.  <br>Must be a float in the range [0, 1]. | 0.5 |
 | `box_detections_per_img` | Maximum number of detections per image, for all classes. <br> Must be a positive integer.| 100 |
 | `tile_grid_size` | The grid size to use for tiling each image. <br>*Note: tile_grid_size must not be None to enable [small object detection](how-to-use-automl-small-object-detect.md) logic*<br> A tuple of two integers passed as a string. Example: --tile_grid_size "(3, 2)" | No Default |
 | `tile_overlap_ratio` | Overlap ratio between adjacent tiles in each dimension. <br> Must be float in the range of [0, 1) | 0.25 |