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Welcome to the Ultralytics YOLOv8 🚀 wiki! Here you'll find useful tutorials, environments, and the current repo status. Please visit https://docs.ultralytics.com also for full YOLOv8 documentation.

Tutorials

Unlock the full potential of YOLOv8 with our comprehensive tutorials! Whether you're looking to train your model, validate its performance, or deploy it in the real-world, we've got you covered. Follow these guides to take your YOLOv8 skills to the next level.

• Quickstart: Get started with YOLOv8 in just a few steps.
• Modes: Overview of different YOLOv8 operation modes.
  • Train: Learn how to train YOLOv8 on your dataset.
  • Val: Validate your trained YOLOv8 model.
  • Predict: Perform object detection with your YOLOv8 model.
  • Export: Export YOLOv8 models to various formats.
  • Track: Apply YOLOv8 for object tracking in videos.
  • Benchmark: Evaluate YOLOv8 performance and compare export formats.
• Tasks: Discover YOLOv8 capabilities for various tasks.
  • Detect: Object detection with YOLOv8.
  • Segment: Instance segmentation using YOLOv8.
  • Classify: Image classification with YOLOv8.
  • Pose: Human pose estimation using YOLOv8.

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.