Home › Vision Models › YOLOv8

YOLOv8 - AI Vision Models Tool

Overview

YOLOv8 is Ultralytics' latest open-source family of real-time object detection, segmentation, and classification models designed for production and research. It unifies detection, instance and semantic segmentation, and image classification into a single PyTorch-based codebase with a concise command-line interface (the `yolo` CLI) and a Python API. The project provides a model zoo of pre-trained weights (YOLOv8n/s/m/l/x), enabling scalable trade-offs between speed, size, and accuracy for edge devices through to server-grade inference. YOLOv8 emphasizes ease-of-use and deployment: users can train custom datasets using simple YAML dataset definitions, run one-line inference, and export models to common deployment runtimes such as ONNX, TensorRT, CoreML, and TFLite. According to the GitHub repository, YOLOv8 includes utilities for data augmentation, evaluation with COCO-style metrics, and experiment logging, making it suitable for rapid prototyping, benchmarking, and production pipelines. The codebase is actively maintained with example notebooks, ready-made training scripts, and a model hub of task-specific checkpoints.

Installation

Install via pip:

pip install ultralytics
yolo --help
yolo detect predict model=yolov8n.pt source='path/to/image_or_video'
yolo detect train data=data.yaml model=yolov8s.pt epochs=100

Key Features

  • Multiple model sizes (yolov8n/s/m/l/x) for edge-to-cloud deployment trade-offs
  • Unified tasks: object detection, instance/semantic segmentation, and classification
  • Simple CLI and Python API for training, validation, inference, and export
  • Export support to ONNX, TensorRT, CoreML, TFLite and standard formats
  • Pre-trained model zoo and sample YAML datasets for quick fine-tuning

Community

YOLOv8 is developed and maintained in an active open-source GitHub repository with regular updates, an issues tracker, example notebooks, and a model zoo. The project has broad community adoption in research and industry, with many third-party tutorials, integrations, and forks. According to the repository, contributors and users exchange bug reports, feature requests, and usage examples via GitHub issues and pull requests, and the project is commonly referenced in computer vision learning resources.

Last Refreshed: 2026-01-09

Key Information

  • Category: Vision Models
  • Type: AI Vision Models Tool
Visit Official Website

Related Tools

Janus-1.3B

A unified multimodal AI model that decouples visual encoding to support both understanding and generation tasks.
YOLOv10

YOLOv10 is a real-time end-to-end object detection tool that improves upon previous YOLO versions through NMS-free training and a comprehensive architectural design to enhance efficiency and accuracy. It offers state-of-the-art performance across various model sizes and is implemented in PyTorch.
BLIP-2

BLIP-2 is an advanced visual-language model that allows zero-shot image-to-text generation, enabling tasks such as image captioning and visual question answering using a combination of pretrained vision and language models.
DeepSeek-VL2

A series of advanced vision-language models designed for multimodal understanding, available in multiple sizes to suit varying complexity and performance requirements.
YOLOv5

YOLOv5 is a popular open-source AI tool aimed at object detection, image segmentation, and image classification, leveraging PyTorch for model building and deployment. It supports various deployment formats including ONNX, CoreML, and TFLite, and is well-documented for ease of use in research and practical applications.
JanusFlow-1.3B

JanusFlow-1.3B is a unified multimodal model by DeepSeek that integrates autoregressive language models with rectified flow, enabling both multimodal understanding and image generation.