{"id":6610,"date":"2026-06-22T18:38:25","date_gmt":"2026-06-22T13:38:25","guid":{"rendered":"https:\/\/cifrum.kz\/?p=6610"},"modified":"2026-06-22T22:32:34","modified_gmt":"2026-06-22T17:32:34","slug":"how-to-install-train-yolo26","status":"publish","type":"post","link":"https:\/\/cifrum.kz\/en\/how-to-install-train-yolo26\/","title":{"rendered":"How to install and train YOLO26: step-by-step guide"},"content":{"rendered":"\n<p class=\"has-large-font-size wp-block-paragraph\"><strong>This guide takes you from an empty folder to a custom YOLO26 model:<\/strong> you will install Ultralytics in an isolated Python environment, run a test prediction, prepare a dataset, train an object detector, evaluate its metrics, and export it to ONNX. The commands work on Windows, macOS, and Linux. A capable GPU is helpful, but it is not required to verify the workflow.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p><strong>Current as of June 22, 2026.<\/strong> The examples were checked against the official <a href=\"https:\/\/docs.ultralytics.com\/models\/yolo26\/\" target=\"_blank\" rel=\"noopener\">YOLO26 documentation<\/a> and the <a href=\"https:\/\/docs.ultralytics.com\/modes\/train\/\" target=\"_blank\" rel=\"noopener\">Train<\/a>, <a href=\"https:\/\/docs.ultralytics.com\/modes\/val\/\" target=\"_blank\" rel=\"noopener\">Val<\/a>, <a href=\"https:\/\/docs.ultralytics.com\/modes\/predict\/\" target=\"_blank\" rel=\"noopener\">Predict<\/a>, and <a href=\"https:\/\/docs.ultralytics.com\/modes\/export\/\" target=\"_blank\" rel=\"noopener\">Export<\/a> guides. The package interface can change, so pin dependency versions for production work.<\/p><\/blockquote>\n\n\n<h2 class=\"wp-block-heading\">What you will have at the end<\/h2>\n<ul class=\"wp-block-list\"><li>an isolated Python environment with <code>ultralytics<\/code> installed;<\/li><li>a working YOLO26 prediction on an image;<\/li><li>a YOLO-format dataset and <code>dataset.yaml<\/code> configuration;<\/li><li>a <code>best.pt<\/code> checkpoint selected by validation performance;<\/li><li>predictions on new images and videos;<\/li><li>an exported ONNX model for deployment.<\/li><\/ul>\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/cifrum.kz\/wp-content\/uploads\/2026\/06\/yolo-workflow-en.jpg\" alt=\"Five YOLO stages: collect, label, train, validate, and export\"\/><figcaption class=\"wp-element-caption\">The practical workflow has five stages. Get an honest baseline before tuning hyperparameters.<\/figcaption><\/figure>\n\n\n<h2 class=\"wp-block-heading\">Which YOLO version and model size should you choose?<\/h2>\n<p class=\"wp-block-paragraph\">Several independent research teams use the YOLO name, so version numbers do not form one continuous official series. This guide uses <strong>Ultralytics YOLO26<\/strong>. Its package provides one CLI and Python API for training, validation, prediction, and export. According to the project documentation, YOLO26 performs end-to-end detection without a separate NMS step by default and is available in <code>n<\/code>, <code>s<\/code>, <code>m<\/code>, <code>l<\/code>, and <code>x<\/code> sizes.<\/p>\n<figure class=\"wp-block-table\"><table><thead><tr><th>Model<\/th><th>When to choose it<\/th><th>Practical advice<\/th><\/tr><\/thead><tbody><tr><td><code>yolo26n.pt<\/code><\/td><td>First experiment, CPU, or edge device<\/td><td>Start here to expose data errors quickly<\/td><\/tr><tr><td><code>yolo26s.pt<\/code><\/td><td>You need more accuracy with moderate resources<\/td><td>Compare it with nano on the same test set<\/td><\/tr><tr><td><code>yolo26m.pt<\/code><\/td><td>You have a GPU and prioritize accuracy<\/td><td>Monitor VRAM and end-to-end latency<\/td><\/tr><tr><td><code>yolo26l.pt<\/code>, <code>yolo26x.pt<\/code><\/td><td>Server GPU and a difficult task<\/td><td>Use them only after a baseline experiment<\/td><\/tr><\/tbody><\/table><\/figure>\n<p class=\"wp-block-paragraph\">YOLO11 and YOLOv8 remain sensible choices for products with an established codebase and tested exports. For a new learning project, start with <code>yolo26n.pt<\/code>. Move to a larger model only after comparing accuracy on your independent test set and speed on the actual target device.<\/p>\n\n<h2 class=\"wp-block-heading\">Requirements before installation<\/h2>\n<ul class=\"wp-block-list\"><li><strong>Python:<\/strong> use a Python version supported by the packages and create a dedicated virtual environment.<\/li><li><strong>Storage:<\/strong> leave room for dependencies, model weights, the dataset, and the <code>runs<\/code> directory.<\/li><li><strong>GPU:<\/strong> NVIDIA CUDA can substantially accelerate training; Apple Silicon can use MPS. A CPU is enough for installation checks, but training is usually much slower.<\/li><li><strong>Data:<\/strong> you need the right to use the images, and they should represent real deployment conditions.<\/li><\/ul>\n\n<h2 class=\"wp-block-heading\">Step 1. Create a project folder and virtual environment<\/h2>\n<p class=\"wp-block-paragraph\">Isolation keeps YOLO dependencies from conflicting with other Python projects. On macOS or Linux, open Terminal:<\/p>\n<pre class=\"wp-block-code\"><code>mkdir yolo-project\ncd yolo-project\npython3 -m venv .venv\nsource .venv\/bin\/activate<\/code><\/pre>\n<p class=\"wp-block-paragraph\">On Windows, open PowerShell:<\/p>\n<pre class=\"wp-block-code\"><code>mkdir yolo-project\ncd yolo-project\npy -m venv .venv\n.venv\\Scripts\\Activate.ps1<\/code><\/pre>\n<p class=\"wp-block-paragraph\">If PowerShell blocks the activation script, do not disable protection system-wide. Allow scripts only for the current process:<\/p>\n<pre class=\"wp-block-code\"><code>Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass\n.venv\\Scripts\\Activate.ps1<\/code><\/pre>\n\n<h2 class=\"wp-block-heading\">Step 2. Install Ultralytics<\/h2>\n<p class=\"wp-block-paragraph\">Upgrade <code>pip<\/code>, install the package from PyPI, and print the version that was actually installed:<\/p>\n<pre class=\"wp-block-code\"><code>python -m pip install --upgrade pip\npython -m pip install ultralytics\npython -c \"import ultralytics; print(ultralytics.__version__)\"\nyolo checks<\/code><\/pre>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/cifrum.kz\/wp-content\/uploads\/2026\/06\/yolo-install-terminal-en.jpg\" alt=\"Commands for creating a Python environment and installing Ultralytics YOLO\"\/><figcaption class=\"wp-element-caption\">Demonstration terminal: Ultralytics is installed in an isolated environment, then <code>yolo checks<\/code> verifies the setup.<\/figcaption><\/figure>\n<p class=\"wp-block-paragraph\">For a Linux server without a graphical display, the official installation guide offers a headless package that avoids GUI-related OpenCV dependencies:<\/p>\n<pre class=\"wp-block-code\"><code>python -m pip install ultralytics-opencv-headless<\/code><\/pre>\n\n<h2 class=\"wp-block-heading\">Step 3. Check CPU, CUDA, or MPS support<\/h2>\n<pre class=\"wp-block-code\"><code>python -c \"import torch; print('PyTorch:', torch.__version__); print('CUDA:', torch.cuda.is_available()); print('MPS:', hasattr(torch.backends, 'mps') and torch.backends.mps.is_available())\"<\/code><\/pre>\n<ul class=\"wp-block-list\"><li><code>CUDA: True<\/code> means PyTorch can see a compatible NVIDIA GPU;<\/li><li><code>MPS: True<\/code> means you can use <code>device=mps<\/code> on a compatible Mac;<\/li><li>if both values are <code>False<\/code>, YOLO will use the CPU unless another device is specified.<\/li><\/ul>\n<p class=\"wp-block-paragraph\">If CUDA is unavailable, run <code>nvidia-smi<\/code> first. A failure at this stage is usually related to the driver, the PyTorch build, or the environment\u2014not the dataset.<\/p>\n\n<h2 class=\"wp-block-heading\">Step 4. Run YOLO26 on a test image<\/h2>\n<p class=\"wp-block-paragraph\">The first run downloads pretrained weights automatically. This smoke test separates installation problems from later annotation problems:<\/p>\n<pre class=\"wp-block-code\"><code>yolo detect predict model=yolo26n.pt \\\n  source=\"https:\/\/ultralytics.com\/images\/bus.jpg\" \\\n  imgsz=640 conf=0.25 save=True<\/code><\/pre>\n<p class=\"wp-block-paragraph\">On Windows, you can run the same command on one line. Ultralytics saves results under <code>runs\/detect\/predict<\/code>, or in the next numbered directory if it already exists.<\/p>\n\n<h2 class=\"wp-block-heading\">Step 5. Define classes and collect images<\/h2>\n<p class=\"wp-block-paragraph\">Write a rule for each class before annotation. For a personal protective equipment detector, the classes might be <code>person<\/code>, <code>helmet<\/code>, and <code>vest<\/code>. Decide how to handle partially visible helmets, reflections, objects printed on signs, and very small instances. More images will not fix contradictory labels.<\/p>\n<ul class=\"wp-block-list\"><li>cover different cameras, distances, lighting, backgrounds, and weather;<\/li><li>include hard negatives where a similar object is present but the target class is not;<\/li><li>do not scatter nearly identical frames from one video across all splits;<\/li><li>set aside an independent <code>test<\/code> split that will not be used for tuning.<\/li><\/ul>\n\n<h2 class=\"wp-block-heading\">Step 6. Annotate data in YOLO format<\/h2>\n<p class=\"wp-block-paragraph\">For object detection, every image has a TXT file with the same base name. Each line describes one object:<\/p>\n<pre class=\"wp-block-code\"><code>class_id x_center y_center width height<\/code><\/pre>\n<p class=\"wp-block-paragraph\">The center coordinates, width, and height are normalized by image dimensions and range from 0 to 1. Class numbering starts at zero. The complete format is documented in the <a href=\"https:\/\/docs.ultralytics.com\/datasets\/detect\/\" target=\"_blank\" rel=\"noopener\">Ultralytics detection dataset guide<\/a>.<\/p>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/cifrum.kz\/wp-content\/uploads\/2026\/06\/yolo-dataset-structure-en.jpg\" alt=\"Images and labels folders with an example YOLO annotation line\"\/><figcaption class=\"wp-element-caption\"><code>001.jpg<\/code> and <code>001.txt<\/code> must live in corresponding split directories and use exactly the same base name.<\/figcaption><\/figure>\n\n<h2 class=\"wp-block-heading\">Step 7. Split the dataset without data leakage<\/h2>\n<p class=\"wp-block-paragraph\">A practical starting point is 70\u201380% of images for <code>train<\/code>, 10\u201320% for <code>val<\/code>, and the remainder for <code>test<\/code>. These percentages are not a universal rule; independence matters more. Keep frames from the same scene, camera, or short video together, otherwise nearly identical images can inflate validation metrics.<\/p>\n\n<h2 class=\"wp-block-heading\">Step 8. Create dataset.yaml<\/h2>\n<pre class=\"wp-block-code\"><code>path: .\/dataset\ntrain: images\/train\nval: images\/val\ntest: images\/test\n\nnames:\n  0: person\n  1: helmet\n  2: vest<\/code><\/pre>\n<p class=\"wp-block-paragraph\">Make sure the <code>names<\/code> mapping matches class identifiers in the TXT files. If class <code>2<\/code> is missing from YAML, an annotation line beginning with <code>2<\/code> is invalid.<\/p>\n\n<h2 class=\"wp-block-heading\">Step 9. Start baseline training<\/h2>\n<p class=\"wp-block-paragraph\">Begin with a pretrained nano model and standard settings. This baseline exposes path, class, and annotation errors before you spend time tuning:<\/p>\n<pre class=\"wp-block-code\"><code>yolo detect train model=yolo26n.pt data=dataset.yaml \\\n  epochs=100 imgsz=640 patience=20 \\\n  project=runs name=helmet-baseline<\/code><\/pre>\n<ul class=\"wp-block-list\"><li><code>epochs=100<\/code> is an upper limit, not a claim that all 100 epochs are necessary;<\/li><li><code>patience=20<\/code> enables early stopping when quality stops improving;<\/li><li><code>imgsz=640<\/code> sets the input size; increasing it consumes more memory and does not guarantee better results;<\/li><li><code>project<\/code> and <code>name<\/code> provide a predictable output path.<\/li><\/ul>\n<pre class=\"wp-block-code\"><code># NVIDIA GPU 0\nyolo detect train model=yolo26n.pt data=dataset.yaml epochs=100 device=0\n\n# Apple Silicon\nyolo detect train model=yolo26n.pt data=dataset.yaml epochs=100 device=mps\n\n# CPU only\nyolo detect train model=yolo26n.pt data=dataset.yaml epochs=30 device=cpu<\/code><\/pre>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/cifrum.kz\/wp-content\/uploads\/2026\/06\/yolo-train-terminal-en.jpg\" alt=\"Example YOLO26 training command and best.pt validation\"\/><figcaption class=\"wp-element-caption\">Demonstration training log: the numbers depend on the dataset and hardware, so the image shows only the shape of the output.<\/figcaption><\/figure>\n\n<h2 class=\"wp-block-heading\">Step 10. Resume interrupted training<\/h2>\n<pre class=\"wp-block-code\"><code>yolo detect train resume \\\n  model=runs\/helmet-baseline\/weights\/last.pt<\/code><\/pre>\n<p class=\"wp-block-paragraph\">Do not start a new experiment over the old one. Use the saved <code>last.pt<\/code> checkpoint and the <code>resume<\/code> option.<\/p>\n\n<h2 class=\"wp-block-heading\">Step 11. Validate best.pt<\/h2>\n<pre class=\"wp-block-code\"><code>yolo detect val \\\n  model=runs\/helmet-baseline\/weights\/best.pt \\\n  data=dataset.yaml plots=True<\/code><\/pre>\n<figure class=\"wp-block-table\"><table><thead><tr><th>Metric<\/th><th>What it tells you<\/th><th>What to inspect when it is low<\/th><\/tr><\/thead><tbody><tr><td>Precision<\/td><td>How often a detection is correct<\/td><td>False positives, similar backgrounds, incorrect boxes<\/td><\/tr><tr><td>Recall<\/td><td>How many real objects the model finds<\/td><td>Missing labels, small or occluded objects<\/td><\/tr><tr><td>mAP50<\/td><td>Average precision at a less strict IoU threshold<\/td><td>General class recognition<\/td><\/tr><tr><td>mAP50\u201395<\/td><td>A stricter score across several IoU thresholds<\/td><td>Box boundaries and annotation consistency<\/td><\/tr><\/tbody><\/table><\/figure>\n<p class=\"wp-block-paragraph\">Open <code>confusion_matrix.png<\/code>, <code>results.png<\/code>, and prediction samples in the run directory. Overall mAP can hide a failure on a rare but important class, so inspect per-class results and review false positives and false negatives manually.<\/p>\n\n<h2 class=\"wp-block-heading\">Step 12. Run the model on your own images and videos<\/h2>\n<pre class=\"wp-block-code\"><code># One image\nyolo detect predict model=runs\/helmet-baseline\/weights\/best.pt \\\n  source=\"test-images\/frame.jpg\" conf=0.25 save=True\n\n# Video\nyolo detect predict model=runs\/helmet-baseline\/weights\/best.pt \\\n  source=\"test-video.mp4\" conf=0.25 save=True\n\n# Webcam 0\nyolo detect predict model=runs\/helmet-baseline\/weights\/best.pt \\\n  source=0 show=True<\/code><\/pre>\n<p class=\"wp-block-paragraph\">Choose the <code>conf<\/code> threshold according to the cost of errors. Raising it may reduce false positives but can increase missed detections. Tune it on a separate dataset, not one favorable image.<\/p>\n\n<h2 class=\"wp-block-heading\">Step 13. Use the model from Python<\/h2>\n<pre class=\"wp-block-code\"><code>from ultralytics import YOLO\n\nmodel = YOLO(\"runs\/helmet-baseline\/weights\/best.pt\")\nresults = model.predict(\n    source=\"test-images\/frame.jpg\",\n    conf=0.25,\n    save=True,\n)\n\nfor result in results:\n    print(result.boxes.xyxy)\n    print(result.boxes.conf)\n    print(result.boxes.cls)<\/code><\/pre>\n\n<h2 class=\"wp-block-heading\">Step 14. Export YOLO to ONNX<\/h2>\n<pre class=\"wp-block-code\"><code>yolo export \\\n  model=runs\/helmet-baseline\/weights\/best.pt \\\n  format=onnx imgsz=640 simplify=True<\/code><\/pre>\n<p class=\"wp-block-paragraph\">ONNX is a useful portable baseline. TensorRT is common for NVIDIA, OpenVINO for Intel, and Core ML for Apple devices. Export is not the end of testing: compare exported-model predictions with <code>best.pt<\/code> and measure latency on the actual target hardware.<\/p>\n<pre class=\"wp-block-code\"><code>yolo benchmark \\\n  model=runs\/helmet-baseline\/weights\/best.pt \\\n  data=dataset.yaml imgsz=640 device=0<\/code><\/pre>\n\n<h2 class=\"wp-block-heading\">Common errors and practical fixes<\/h2>\n<h3 class=\"wp-block-heading\">FileNotFoundError or dataset images not found<\/h3><p>Check the working directory, the <code>path<\/code> value in <code>dataset.yaml<\/code>, and filename capitalization. On Linux, <code>Images<\/code> and <code>images<\/code> are different paths.<\/p>\n<h3 class=\"wp-block-heading\">CUDA out of memory<\/h3><p>Reduce <code>batch<\/code> first, then lower <code>imgsz<\/code> or choose a smaller model. Close processes using VRAM and inspect them with <code>nvidia-smi<\/code>.<\/p>\n<pre class=\"wp-block-code\"><code>yolo detect train model=yolo26n.pt data=dataset.yaml \\\n  epochs=100 imgsz=640 batch=4 device=0<\/code><\/pre>\n<h3 class=\"wp-block-heading\">The model detects nothing<\/h3><p>Temporarily lower <code>conf<\/code> and inspect raw predictions. Verify class order, annotation coordinates, missing labels, and the difference between new frames and training data.<\/p>\n<h3 class=\"wp-block-heading\">High mAP but poor real-world performance<\/h3><p>Typical causes are leakage between train and val, repetitive data, or domain shift. Build an independent test set from a different camera, day, site, or production batch.<\/p>\n<h3 class=\"wp-block-heading\">The yolo command is not found<\/h3><p>Reactivate the environment and run <code>python -m pip show ultralytics<\/code>. Make sure <code>python<\/code> and <code>pip<\/code> refer to the same environment.<\/p>\n\n<h2 class=\"wp-block-heading\">Licensing considerations before commercial deployment<\/h2>\n<p class=\"wp-block-paragraph\">The Ultralytics repository and models are offered under AGPL-3.0 and a separate Enterprise license. Open source does not mean there are no obligations. Before embedding the model in a closed product, review the <a href=\"https:\/\/github.com\/ultralytics\/ultralytics\" target=\"_blank\" rel=\"noopener\">repository terms<\/a>, model weights, and distribution method. Seek specialist licensing advice for legally significant decisions.<\/p>\n\n<h2 class=\"wp-block-heading\">Frequently asked questions<\/h2>\n<h3 class=\"wp-block-heading\">Can I train YOLO without a GPU?<\/h3><p>Yes. A CPU is enough to verify the pipeline and run a small experiment, but training usually takes substantially longer. A compatible GPU or cloud environment is more practical for a serious dataset.<\/p>\n<h3 class=\"wp-block-heading\">How many images do I need?<\/h3><p>There is no universal number. Class difficulty, variety, and annotation quality matter more than raw volume. Start with a small but diverse set, train a baseline, and collect data targeted at observed failures.<\/p>\n<h3 class=\"wp-block-heading\">Why use best.pt instead of last.pt?<\/h3><p><code>best.pt<\/code> corresponds to the strongest validation result during the run. <code>last.pt<\/code> stores the final epoch state and is primarily useful for resuming training.<\/p>\n<h3 class=\"wp-block-heading\">YOLOv8, YOLO11, or YOLO26?<\/h3><p>For an existing product, keep the version whose integration and export have been tested until a replacement passes accuracy, speed, and deployment tests. For a new project, YOLO26 is the current Ultralytics line as of this article; YOLO11 and YOLOv8 remain useful when your required integration is already proven with them.<\/p>\n<h3 class=\"wp-block-heading\">How do I know the model is production-ready?<\/h3><p>It should pass an independent test set, per-class review, difficult and negative examples, full-pipeline latency measurement, and testing on the target device. One high validation mAP score is not enough.<\/p>\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n<p class=\"wp-block-paragraph\">A reliable custom YOLO model starts with a reproducible baseline, not the largest architecture. Install Ultralytics in an isolated environment, verify <code>yolo26n.pt<\/code>, split your data correctly, train the model, and inspect its failures. Only then should you change model size or hyperparameters. This keeps each experiment interpretable and makes deployment decisions evidence-based.<\/p>\n<p class=\"wp-block-paragraph\"><em>The featured image was created with a generative model. Step-by-step images are anonymized demonstration screens and diagrams; they contain no real accounts, keys, passwords, or home-directory paths.<\/em><\/p>\n\n\n<style>\nbody.postid-6610 .cf-code-copy-wrap{position:relative;margin:1.5em 0}body.postid-6610 .cf-code-copy-wrap pre.wp-block-code{margin:0;padding-top:3.25rem;overflow-x:auto}body.postid-6610 .cf-code-copy-button{position:absolute;top:.75rem;right:.75rem;z-index:2;border:1px solid #cbd4e5;border-radius:5px;background:#fff;color:#17305f;padding:.45rem .75rem;font-size:.85rem;font-weight:600;line-height:1.2;cursor:pointer}body.postid-6610 .cf-code-copy-button:hover,body.postid-6610 .cf-code-copy-button:focus{background:#eef3ff;border-color:#405ccb;outline:none}@media(max-width:600px){body.postid-6610 .cf-code-copy-button{font-size:.78rem;padding:.4rem .6rem}body.postid-6610 .wp-block-table{overflow-x:auto}}\n<\/style>\n<script>\ndocument.addEventListener('DOMContentLoaded',function(){document.querySelectorAll('body.postid-6610 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failed');}).then(function(){button.textContent='Copied';window.setTimeout(function(){button.textContent='Copy'},1600);});});wrap.insertBefore(button,pre);});});\n<\/script>\n\n","protected":false},"excerpt":{"rendered":"<p>Install Ultralytics YOLO26 on Windows, macOS, or Linux, prepare a dataset, train and validate a custom model, run inference, and export 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