--- comments: true description: Master YOLO with Ultralytics tutorials covering training, deployment and optimization. Find solutions, improve metrics, and deploy with ease!. keywords: Ultralytics, YOLO, tutorials, guides, object detection, deep learning, PyTorch, training, deployment, optimization, computer vision --- # Comprehensive Tutorials to Ultralytics YOLO Welcome to the Ultralytics' YOLO 🚀 Guides! Our comprehensive tutorials cover various aspects of the YOLO [object detection](https://www.ultralytics.com/glossary/object-detection) model, ranging from training and prediction to deployment. Built on [PyTorch](https://www.ultralytics.com/glossary/pytorch), YOLO stands out for its exceptional speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) in real-time object detection tasks. Whether you're a beginner or an expert in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl), our tutorials offer valuable insights into the implementation and optimization of YOLO for your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) projects. Let's dive in!



Watch: Ultralytics YOLOv8 Guides Overview

## Guides Here's a compilation of in-depth guides to help you master different aspects of Ultralytics YOLO. - [YOLO Common Issues](yolo-common-issues.md) ⭐ RECOMMENDED: Practical solutions and troubleshooting tips to the most frequently encountered issues when working with Ultralytics YOLO models. - [YOLO Performance Metrics](yolo-performance-metrics.md) ⭐ ESSENTIAL: Understand the key metrics like mAP, IoU, and [F1 score](https://www.ultralytics.com/glossary/f1-score) used to evaluate the performance of your YOLO models. Includes practical examples and tips on how to improve detection accuracy and speed. - [Model Deployment Options](model-deployment-options.md): Overview of YOLO [model deployment](https://www.ultralytics.com/glossary/model-deployment) formats like ONNX, OpenVINO, and TensorRT, with pros and cons for each to inform your deployment strategy. - [K-Fold Cross Validation](kfold-cross-validation.md) 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique. - [Hyperparameter Tuning](hyperparameter-tuning.md) 🚀 NEW: Discover how to optimize your YOLO models by fine-tuning hyperparameters using the Tuner class and genetic evolution algorithms. - [SAHI Tiled Inference](sahi-tiled-inference.md) 🚀 NEW: Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLOv8 for object detection in high-resolution images. - [AzureML Quickstart](azureml-quickstart.md) 🚀 NEW: Get up and running with Ultralytics YOLO models on Microsoft's Azure [Machine Learning](https://www.ultralytics.com/glossary/machine-learning-ml) platform. Learn how to train, deploy, and scale your object detection projects in the cloud. - [Conda Quickstart](conda-quickstart.md) 🚀 NEW: Step-by-step guide to setting up a [Conda](https://anaconda.org/conda-forge/ultralytics) environment for Ultralytics. Learn how to install and start using the Ultralytics package efficiently with Conda. - [Docker Quickstart](docker-quickstart.md) 🚀 NEW: Complete guide to setting up and using Ultralytics YOLO models with [Docker](https://hub.docker.com/r/ultralytics/ultralytics). Learn how to install Docker, manage GPU support, and run YOLO models in isolated containers for consistent development and deployment. - [Raspberry Pi](raspberry-pi.md) 🚀 NEW: Quickstart tutorial to run YOLO models to the latest Raspberry Pi hardware. - [NVIDIA Jetson](nvidia-jetson.md) 🚀 NEW: Quickstart guide for deploying YOLO models on NVIDIA Jetson devices. - [DeepStream on NVIDIA Jetson](deepstream-nvidia-jetson.md) 🚀 NEW: Quickstart guide for deploying YOLO models on NVIDIA Jetson devices using DeepStream and TensorRT. - [Triton Inference Server Integration](triton-inference-server.md) 🚀 NEW: Dive into the integration of Ultralytics YOLOv8 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments. - [YOLO Thread-Safe Inference](yolo-thread-safe-inference.md) 🚀 NEW: Guidelines for performing inference with YOLO models in a thread-safe manner. Learn the importance of thread safety and best practices to prevent race conditions and ensure consistent predictions. - [Isolating Segmentation Objects](isolating-segmentation-objects.md) 🚀 NEW: Step-by-step recipe and explanation on how to extract and/or isolate objects from images using Ultralytics Segmentation. - [Edge TPU on Raspberry Pi](coral-edge-tpu-on-raspberry-pi.md): [Google Edge TPU](https://coral.ai/products/accelerator) accelerates YOLO inference on [Raspberry Pi](https://www.raspberrypi.com/). - [View Inference Images in a Terminal](view-results-in-terminal.md): Use VSCode's integrated terminal to view inference results when using Remote Tunnel or SSH sessions. - [OpenVINO Latency vs Throughput Modes](optimizing-openvino-latency-vs-throughput-modes.md) - Learn latency and throughput optimization techniques for peak YOLO inference performance. - [Steps of a Computer Vision Project ](steps-of-a-cv-project.md) 🚀 NEW: Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results. - [Defining A Computer Vision Project's Goals](defining-project-goals.md) 🚀 NEW: Walk through how to effectively define clear and measurable goals for your computer vision project. Learn the importance of a well-defined problem statement and how it creates a roadmap for your project. - [Data Collection and Annotation](data-collection-and-annotation.md) 🚀 NEW: Explore the tools, techniques, and best practices for collecting and annotating data to create high-quality inputs for your computer vision models. - [Preprocessing Annotated Data](preprocessing_annotated_data.md) 🚀 NEW: Learn about preprocessing and augmenting image data in computer vision projects using YOLOv8, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA). - [Tips for Model Training](model-training-tips.md) 🚀 NEW: Explore tips on optimizing [batch sizes](https://www.ultralytics.com/glossary/batch-size), using [mixed precision](https://www.ultralytics.com/glossary/mixed-precision), applying pre-trained weights, and more to make training your computer vision model a breeze. - [Insights on Model Evaluation and Fine-Tuning](model-evaluation-insights.md) 🚀 NEW: Gain insights into the strategies and best practices for evaluating and fine-tuning your computer vision models. Learn about the iterative process of refining models to achieve optimal results. - [A Guide on Model Testing](model-testing.md) 🚀 NEW: A thorough guide on testing your computer vision models in realistic settings. Learn how to verify accuracy, reliability, and performance in line with project goals. - [Best Practices for Model Deployment](model-deployment-practices.md) 🚀 NEW: Walk through tips and best practices for efficiently deploying models in computer vision projects, with a focus on optimization, troubleshooting, and security. - [Maintaining Your Computer Vision Model](model-monitoring-and-maintenance.md) 🚀 NEW: Understand the key practices for monitoring, maintaining, and documenting computer vision models to guarantee accuracy, spot anomalies, and mitigate data drift. - [ROS Quickstart](ros-quickstart.md) 🚀 NEW: Learn how to integrate YOLO with the Robot Operating System (ROS) for real-time object detection in robotics applications, including Point Cloud and Depth images. ## 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](../help/contributing.md) 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 🙏! ## FAQ ### How do I train a custom object detection model using Ultralytics YOLO? Training a custom object detection model with Ultralytics YOLO is straightforward. Start by preparing your dataset in the correct format and installing the Ultralytics package. Use the following code to initiate training: !!! example === "Python" ```python from ultralytics import YOLO model = YOLO("yolov8s.pt") # Load a pre-trained YOLO model model.train(data="path/to/dataset.yaml", epochs=50) # Train on custom dataset ``` === "CLI" ```bash yolo task=detect mode=train model=yolov8s.pt data=path/to/dataset.yaml epochs=50 ``` For detailed dataset formatting and additional options, refer to our [Tips for Model Training](model-training-tips.md) guide. ### What performance metrics should I use to evaluate my YOLO model? Evaluating your YOLO model performance is crucial to understanding its efficacy. Key metrics include [Mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP), [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU), and F1 score. These metrics help assess the accuracy and [precision](https://www.ultralytics.com/glossary/precision) of object detection tasks. You can learn more about these metrics and how to improve your model in our [YOLO Performance Metrics](yolo-performance-metrics.md) guide. ### Why should I use Ultralytics HUB for my computer vision projects? Ultralytics HUB is a no-code platform that simplifies managing, training, and deploying YOLO models. It supports seamless integration, real-time tracking, and cloud training, making it ideal for both beginners and professionals. Discover more about its features and how it can streamline your workflow with our [Ultralytics HUB](https://docs.ultralytics.com/hub/) quickstart guide. ### What are the common issues faced during YOLO model training, and how can I resolve them? Common issues during YOLO model training include data formatting errors, model architecture mismatches, and insufficient [training data](https://www.ultralytics.com/glossary/training-data). To address these, ensure your dataset is correctly formatted, check for compatible model versions, and augment your training data. For a comprehensive list of solutions, refer to our [YOLO Common Issues](yolo-common-issues.md) guide. ### How can I deploy my YOLO model for real-time object detection on edge devices? Deploying YOLO models on edge devices like NVIDIA Jetson and Raspberry Pi requires converting the model to a compatible format such as TensorRT or TFLite. Follow our step-by-step guides for [NVIDIA Jetson](nvidia-jetson.md) and [Raspberry Pi](raspberry-pi.md) deployments to get started with real-time object detection on edge hardware. These guides will walk you through installation, configuration, and performance optimization.