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!
Here's a compilation of in-depth guides to help you master different aspects of Ultralytics YOLO.
- K-Fold Cross Validation 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique.
- Hyperparameter Tuning 🚀 NEW: Discover how to optimize your YOLO models by fine-tuning hyperparameters using the Tuner class and genetic evolution algorithms.
- Using YOLOv8 with SAHI for Sliced Inference 🚀 NEW: Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLOv8 for object detection in high-resolution images.
- AzureML Quickstart 🚀 NEW: Get up and running with Ultralytics YOLO models on Microsoft's Azure Machine Learning platform. Learn how to train, deploy, and scale your object detection projects in the cloud.
- Conda Quickstart 🚀 NEW: Step-by-step guide to setting up a Conda environment for Ultralytics. Learn how to install and start using the Ultralytics package efficiently with Conda.
Contribute to Our Guides
We welcome contributions from the community! If you've mastered a particular aspect of Ultralytics YOLO that's not yet covered in our guides, we encourage you to share your expertise. Writing a guide is a great way to give back to the community and help us make our documentation more comprehensive and user-friendly.
To get started, please read our Contributing Guide for guidelines on how to open up a Pull Request (PR) 🛠️. We look forward to your contributions!
Let's work together to make the Ultralytics YOLO ecosystem more robust and versatile 🙏!