Learn computer vision with Ultralytics YOLO

Learn the judgment behind every working vision system — framing the task, choosing among the seven Ultralytics YOLO tasks, auditing data and labels, splitting honestly, and reading metrics — with runnable YOLO26 examples on real images and datasets.

A hands-on playbook for the work before training: turn a business goal into an acceptance test, specify and collect representative data, label consistently, audit quality with scripts, split without leakage, tune augmentation, and gate the first Ultralytics YOLO26 fine-tune on a readiness check.

Take Ultralytics YOLO26 from a fresh install to a custom-trained, validated, and exported detector. You'll predict on images, lay out a real dataset, train and diagnose a pill detector, run models on video with tracking, and export to ONNX with proof that it matches PyTorch. Every step runs in Python and on the CLI, on a laptop CPU, with the real output shown. Planning, collecting, and labeling the dataset itself is covered in Building High-Performance YOLO Datasets.

You can train a detector. Ultralytics YOLO supports seven vision tasks, and this course teaches the five you will meet most often after detection — instance and semantic segmentation, pose, oriented bounding boxes, and classification — from label format and training to reading results in code, chaining models, and per-task evaluation and export. Depth estimation, the seventh task, is part of the task plan and the evaluation and export lessons.

Take a trained Ultralytics YOLO model into production: pick a runtime, export and verify it, optimize for your hardware, track objects, build counting pipelines, run many streams, and catch drift before users do.

Run a real detection project on Ultralytics Platform: budget it, ingest and label data, prove the splits are clean, train and compare runs, deploy a dedicated endpoint, monitor accuracy, and automate the retraining loop.