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Foundation pathwaybeginner ~4 hours 10 lessons Final exam · Certificate

From pip install to a custom-trained detector

Train your first YOLO model

Take Ultralytics YOLO from zero to a model trained on your own data — installation, dataset prep, training, evaluation, video inference, and export.

By Ultralytics Academy

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Ultralytics YOLO setup
What you'll learn
Train, validate, and export an Ultralytics YOLO model on a custom dataset, and run it on video — all from the command line and Python.

  • Install Ultralytics and run Ultralytics YOLO predictions in Python and on the CLI.

  • Pick the right model size for your latency, accuracy, and hardware budget.

  • Convert a custom dataset into the Ultralytics YOLO label format and write a data.yaml.

  • Train, validate, and resume training with sensible defaults.

  • Export to ONNX or TensorRT for deployment.

What you'll build

  • A small custom dataset in Ultralytics YOLO format with a data.yaml.

  • A trained best.pt checkpoint that beats the pretrained baseline on your data.

  • A short video inference script that overlays detections.

  • An exported model file (ONNX or engine) ready for your runtime.

Prerequisites

  • Comfortable running Python and shell commands.

  • An NVIDIA GPU with CUDA available will make training faster but is not required — Apple Silicon and CPU work too.

  • Recommended: complete Computer Vision Foundations first.

Course content

5 modules · 10 lessons

Module 1

Setup
1

Install Ultralytics

Get a working environment in one command — and verify GPU access.

Module 2

Predict
2

Predict on an Image

Run a pretrained Ultralytics YOLO model and read the result object — boxes, confidences, classes.

3

Choose an Ultralytics YOLO Model Size

Nano, small, medium, large, x-large — what each one costs and where each one wins.

Module 3

Data
4

Prepare a Custom Dataset

Convert images and labels into the Ultralytics YOLO format — folders, files, normalized coordinates.

5

Write a Dataset YAML

The single config file that connects your folders to YOLO's training loop.

Module 4

Train
6

Train Your First Model

One Python call, sensible defaults, and a checkpoint at the end.

7

Validate and Diagnose

Beyond the single mAP number — per-class AP, confusion matrices, and what they tell you to do next.

8

Resume and Iterate

Continue from where training stopped, fine-tune best.pt, and run experiments cheaply.

Module 5

Ship
9

Run Inference on Video

From single-image predict to real-time video — and what changes when frames are continuous.

10

Export for Deployment

ONNX, TensorRT, CoreML, OpenVINO — pick the format your runtime actually wants.