---
title: YOLO How-To Guides
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 for Ultralytics YOLO

Welcome to 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.

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  <strong>Watch:</strong> Ultralytics YOLO26 Guides Overview
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## Guides

Guides are grouped by project stage — from environment setup and data preparation through training, evaluation, deployment, inference optimization, and edge hardware — so you can jump straight to the step you're working on.

### Get started & plan

- [Conda Quickstart](conda-quickstart.md): 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): 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.
- [AzureML Quickstart](azureml-quickstart.md): 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.
- [Modal Quickstart](modal-quickstart.md): Run Ultralytics YOLO26 on Modal's serverless cloud platform, covering authentication, GPU inference, and training jobs with persistent storage — no infrastructure to manage.
- [Defining A Computer Vision Project's Goals](defining-project-goals.md): 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.
- [Steps of a Computer Vision Project](steps-of-a-cv-project.md): Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results.
- [YOLO Architecture Explained](yolo-architecture.md): Trace how the YOLO architecture evolved from YOLOv3 to YOLO26 across backbone, neck, and detection head, covering C2f, C3k2, C2PSA attention, anchor-free, and NMS-free design.

### Prepare your data

- [Data Collection and Annotation](data-collection-and-annotation.md): 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): Learn data preprocessing for computer vision with YOLO26, including image resizing, normalization, dataset splitting, data augmentation, and exploratory data analysis (EDA).
- [YOLO Data Augmentation](yolo-data-augmentation.md): Master the complete range of data augmentation techniques in YOLO, from basic transformations to advanced strategies for improving model robustness and performance.
- [COCO to YOLO Conversion](coco-to-yolo.md): Complete guide to converting COCO JSON annotations to YOLO format for training. Covers detection, segmentation, and keypoints, including class ID mapping and common conversion pitfalls.
- [COCO JSON Training](coco-json-training.md): Train YOLO directly on COCO JSON annotations without converting to YOLO format, using a custom dataset class and trainer.

### Train & fine-tune

- [Tips for Model Training](model-training-tips.md): Explore tips on optimizing [batch sizes](https://www.ultralytics.com/glossary/batch-size), using [mixed precision](https://www.ultralytics.com/glossary/mixed-precision), applying pretrained weights, and more to make training your computer vision model a breeze.
- [Fine-Tuning YOLO on Custom Data](finetuning-guide.md): Complete guide to fine-tuning YOLO26 on custom datasets with pretrained weights, covering transfer learning, layer freezing, optimizer selection, two-stage training, and troubleshooting.
- [YOLO26 Training Recipe](yolo26-training-recipe.md): Full documentation of the hyperparameters, augmentation pipelines, and optimizer settings used to train the official YOLO26 base checkpoints on COCO, with practical fine-tuning guidance.
- [Hyperparameter Tuning](hyperparameter-tuning.md): Discover how to optimize your YOLO models by fine-tuning hyperparameters using the Tuner class and genetic evolution algorithms.
- [Knowledge Distillation](knowledge-distillation.md): Learn how to improve model performance by transferring knowledge from a larger teacher model to a smaller student model using feature-based distillation.
- [K-Fold Cross Validation](kfold-cross-validation.md): Learn how to improve model generalization using K-Fold cross-validation technique.
- [Customizing Trainer](custom-trainer.md): Learn how to subclass the YOLO trainer to log custom metrics, add class-weighted loss, customize model saving, freeze/unfreeze the backbone, and set per-layer learning rates.
- [Model YAML Configuration Guide](model-yaml-config.md): A comprehensive deep dive into Ultralytics' model architecture definitions. Explore the YAML format, understand the module resolution system, and learn how to integrate custom modules seamlessly.

### Evaluate & troubleshoot

- [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.
- [Insights on Model Evaluation and Fine-Tuning](model-evaluation-insights.md): Evaluate computer vision models with metrics like mAP and IoU, then fine-tune training parameters such as learning-rate warmup to improve detection accuracy.
- [Model Testing](model-testing.md): Learn how to test computer vision models on unseen data, validate YOLO26 models, and catch overfitting, underfitting, and data leakage before deployment.
- [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.
- [Maintaining Your Computer Vision Model](model-monitoring-and-maintenance.md): Understand the key practices for monitoring, maintaining, and documenting computer vision models to guarantee accuracy, spot anomalies, and mitigate data drift.

### Deploy & serve

- [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.
- [Best Practices for Model Deployment](model-deployment-practices.md): Walk through tips and best practices for efficiently deploying models in computer vision projects, with a focus on optimization, troubleshooting, and security.
- [Export Non-YOLO Models](export-non-yolo-models.md): Use Ultralytics standalone export utilities to convert any `torch.nn.Module` (timm, torchvision, custom) to ONNX, TorchScript, OpenVINO, CoreML, NCNN, MNN, PaddlePaddle, ExecuTorch, and TensorFlow SavedModel.
- [End-to-End Detection](end2end-detection.md): Understand YOLO26's NMS-free end-to-end detection, export compatibility, output format changes, and how to migrate from older YOLO models.
- [Triton Inference Server Integration](triton-inference-server.md): Dive into the integration of Ultralytics YOLO26 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
- [YOLO Thread-Safe Inference](yolo-thread-safe-inference.md): 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.
- [ROS Quickstart](ros-quickstart.md): 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.
- [Vertex AI Deployment with Docker](vertex-ai-deployment-with-docker.md): Streamlined guide to containerizing YOLO models with Docker and deploying them on Google Cloud Vertex AI—covering build, push, autoscaling, and monitoring.

### Optimize inference

- [SAHI Tiled Inference](sahi-tiled-inference.md): Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLO26 for object detection in high-resolution images.
- [OpenVINO Latency vs Throughput Modes](optimizing-openvino-latency-vs-throughput-modes.md): Learn latency and throughput optimization techniques for peak YOLO inference performance.
- [NVIDIA DALI GPU Preprocessing](nvidia-dali.md): Eliminate CPU preprocessing bottlenecks by running YOLO letterbox resize, padding, and normalization on the GPU using NVIDIA DALI, with Triton Inference Server integration.
- [Isolating Segmentation Objects](isolating-segmentation-objects.md): Step-by-step recipe and explanation on how to extract and/or isolate objects from images using Ultralytics Segmentation.
- [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.

### Edge & embedded hardware

- [NVIDIA Jetson](nvidia-jetson.md): Quickstart guide for deploying YOLO models on NVIDIA Jetson devices.
- [Raspberry Pi](raspberry-pi.md): Quickstart tutorial to run YOLO models on the latest Raspberry Pi hardware.
- [DeepStream on NVIDIA Jetson](deepstream-nvidia-jetson.md): Quickstart guide for deploying YOLO models on NVIDIA Jetson devices using DeepStream and TensorRT.
- [Edge TPU on Raspberry Pi](coral-edge-tpu-on-raspberry-pi.md): [Google Edge TPU](https://developers.google.com/coral) accelerates YOLO inference on [Raspberry Pi](https://www.raspberrypi.com/).
- [NVIDIA DGX Spark](nvidia-dgx-spark.md): Quickstart guide for deploying YOLO models on NVIDIA DGX Spark devices.
- [DL Streamer on Intel](dlstreamer-intel.md): Quickstart guide for deploying YOLO26 models on Intel Core Ultra Series 3 devices using DL Streamer and OpenVINO™.

## 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 a Pull Request (PR). When adding a new guide, place it under the category above that matches its project stage and keep it to a single page type — a step-by-step tutorial, a task-focused how-to, or a reference — so the collection stays easy to browse as it grows. We look forward to your contributions.

## 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("yolo26n.pt")  # Load a pretrained YOLO model
        model.train(data="path/to/dataset.yaml", epochs=50)  # Train on custom dataset
        ```

    === "CLI"

        ```bash
        yolo task=detect mode=train model=yolo26n.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 Platform for my computer vision projects?

Ultralytics Platform 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 Platform](../platform/index.md) 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 LiteRT. 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.
