Home › Vision Models › Ultralytics YOLO11

Ultralytics YOLO11 - AI Vision Models Tool

Overview

Ultralytics YOLO11 is the latest continuation of Ultralytics' open-source computer vision suite, providing end-to-end tools for object detection, instance segmentation, pose estimation and image classification. The project bundles pretrained model weights, a high-level Python API, a single-line CLI for training and inference, and utilities to export models to production runtimes (ONNX, TensorRT, CoreML, etc.). It is designed for research and production workflows, with emphasis on straightforward dataset handling, reproducible training, and real-time inference on edge devices. The repository is tightly integrated with Ultralytics HUB for remote dataset visualization, experiment tracking, and hosted training jobs, allowing teams to iterate on models without managing infrastructure. According to the GitHub repository, the project is actively maintained (last commit 2026-01-09) and has a large community of contributors, pretrained model checkpoints, example notebooks, and conversion/export utilities to deploy models across platforms and accelerators.

GitHub Statistics

  • Stars: 50,922
  • Forks: 9,830
  • Contributors: 317
  • License: AGPL-3.0
  • Primary Language: Python
  • Last Updated: 2026-01-09T11:54:32Z
  • Latest Release: v8.3.250

According to the GitHub repository, Ultralytics has 50,922 stars, 9,830 forks and 317 contributors, with the most recent commit on 2026-01-09 (license: AGPL-3.0). These metrics indicate a large, active community and steady development cadence. The high contributor count and frequent commits suggest ongoing feature additions, bug fixes, and model improvements. The AGPL-3.0 license encourages open collaboration but can be restrictive for some commercial uses.

Installation

Install via pip:

pip install -U ultralytics
git clone https://github.com/ultralytics/ultralytics.git
cd ultralytics && pip install -e .

Key Features

  • Unified models for detection, segmentation, pose estimation and classification in one repository
  • High-level Python API and single-line CLI for training and inference workflows
  • Pretrained model zoo (small→large) optimized for speed and accuracy on common benchmarks
  • Export utilities to ONNX, TensorRT, CoreML and TorchScript for cross-platform deployment
  • Integration with Ultralytics HUB for remote visualization, dataset management and training
  • Support for real-time inference and edge deployment with lightweight model variants
  • Utilities for dataset conversion, augmentation, mixed-precision training and evaluation

Community

Large, active community with 317 contributors and extensive GitHub activity. Users and contributors provide tutorials, Colab notebooks and third-party integrations; common praise centers on ease-of-use and performance, while the AGPL license is noted as a commercial consideration.

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.