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ultralytics 8.0.177 add https://youtube.com/ultralytics videos to Docs (#4875)

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docs/modes/val.md
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@ -4,9 +4,31 @@ description: Guide for Validating YOLOv8 Models. Learn how to evaluate the perfo
keywords: Ultralytics, YOLO Docs, YOLOv8, validation, model evaluation, hyperparameters, accuracy, metrics, Python, CLI
---
# Model Validation with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
**Val mode** is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
## Introduction
Validation is a critical step in the machine learning pipeline, allowing you to assess the quality of your trained models. Val mode in Ultralytics YOLOv8 provides a robust suite of tools and metrics for evaluating the performance of your object detection models. This guide serves as a complete resource for understanding how to effectively use the Val mode to ensure that your models are both accurate and reliable.
## Why Validate with Ultralytics YOLO?
Here's why using YOLOv8's Val mode is advantageous:
- **Precision:** Get accurate metrics like mAP50, mAP75, and mAP50-95 to comprehensively evaluate your model.
- **Convenience:** Utilize built-in features that remember training settings, simplifying the validation process.
- **Flexibility:** Validate your model with the same or different datasets and image sizes.
- **Hyperparameter Tuning:** Use validation metrics to fine-tune your model for better performance.
### Key Features of Val Mode
These are the notable functionalities offered by YOLOv8's Val mode:
- **Automated Settings:** Models remember their training configurations for straightforward validation.
- **Multi-Metric Support:** Evaluate your model based on a range of accuracy metrics.
- **CLI and Python API:** Choose from command-line interface or Python API based on your preference for validation.
- **Data Compatibility:** Works seamlessly with datasets used during the training phase as well as custom datasets.
!!! tip "Tip"