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Docs Prettier reformat (#13483)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-06-10 12:59:01 +02:00 • committed by GitHub
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docs/en/modes/val.md
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@ -80,7 +80,7 @@ Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need t
When validating YOLO models, several arguments can be fine-tuned to optimize the evaluation process. These arguments control aspects such as input image size, batch processing, and performance thresholds. Below is a detailed breakdown of each argument to help you customize your validation settings effectively.
| Argument | Type | Default | Description |
|---------------|---------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ------------- | ------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `data` | `str` | `None` | Specifies the path to the dataset configuration file (e.g., `coco8.yaml`). This file includes paths to validation data, class names, and number of classes. |
| `imgsz` | `int` | `640` | Defines the size of input images. All images are resized to this dimension before processing. |
| `batch` | `int` | `16` | Sets the number of images per batch. Use `-1` for AutoBatch, which automatically adjusts based on GPU memory availability. |