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Add new K-Fold cross validation guide in Docs (#3975)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Burhan 2023-07-28 08:57:24 -04:00 • committed by GitHub
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---
comments: true
description: In-depth exploration of Ultralytics' YOLO. Learn about the YOLO object detection model, how to train it on custom data, multi-GPU training, exporting, predicting, deploying, and more.
keywords: Ultralytics, YOLO, Deep Learning, Object detection, PyTorch, Tutorial, Multi-GPU training, Custom data training
---
# Comprehensive Tutorials to Ultralytics YOLO
Welcome to the Ultralytics' YOLO 🚀 Guides! Our comprehensive tutorials cover various aspects of the YOLO object detection model, ranging from training and prediction to deployment. Built on PyTorch, YOLO stands out for its exceptional speed and accuracy in real-time object detection tasks.
Whether you're a beginner or an expert in deep learning, our tutorials offer valuable insights into the implementation and optimization of YOLO for your computer vision projects. Let's dive in!
## Guides
Here's a compilation of in-depth guides to help you master different aspects of Ultralytics YOLO.
* [K-Fold Cross Validation](kfold-cross-validation.md) 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique.
Note: More guides about training, exporting, predicting, and deploying with Ultralytics YOLO are coming soon. Stay tuned!