table link

Author: nibaccam

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

@@ -190,7 +190,7 @@ The model algorithm is required and is passed in via `model_name` parameter. You
 
 ### Supported model algorithms
 
-The following table summarizes the supported models for each computer vision task. See the reference documentation for model specific hyperparameters. 
+The following table summarizes the supported models for each computer vision task.
 
 Task |  Model algorithms | String literal syntax<br> ***`default_model`\**** denoted with \*
 ---|----------|----------

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

@@ -19,6 +19,20 @@ Learn which hyperparameters are available specifically for computer vision tasks
 
 With support for computer vision tasks, you can control the model algorithm and sweep hyperparameters. These model algorithms and hyperparameters are passed in as the parameter space for the sweep. While many of the hyperparameters exposed are model-agnostic, there are instances where hyperparameters are task-specific or model-specific.
 
+## Model-specific hyperparameters
+
+This table summarizes hyperparameters specific to the `yolov5` algorithm.
+
+| Parameter name       | Description           | Default  |
+| ------------- |-------------|----|
+| `validation_metric_type` | Metric computation method to use for validation metrics.  <br> Must be `none`, `coco`, `voc`, or `coco_voc`. | `voc` |
+| `img_size` | Image size for train and validation. <br> Must be a positive integer. <br> <br> *Note: training run may get into CUDA OOM if the size is too big*. | 640 |
+| `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 |
+| `box_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
 
 The following table describes the hyperparameters that are model agnostic.