New YOLO models live in ultralytics/ultralytics 🚀

[glenn-jocher]

YOLOv3 remains available here for legacy projects, historical reproducibility, and users who specifically need this repository.

For all new Ultralytics models, current documentation, active development, and production workflows, use the main repository:

https://github.com/ultralytics/ultralytics

Use the main repo for new projects

The ultralytics/ultralytics repository is the canonical home for the current Ultralytics YOLO family, including:

• YOLO26, the latest stable recommended model generation
• YOLO11 and YOLOv8 support
• Detection, segmentation, semantic segmentation, classification, pose, OBB, and tracking
• Current training, validation, prediction, export, and deployment workflows
• Latest docs, examples, bug fixes, and releases

Install the current package

pip install -U ultralytics

Run YOLO26 from the current package

# Predict
yolo predict model=yolo26n.pt source=https://ultralytics.com/images/bus.jpg imgsz=640

# Train
yolo train data=coco8.yaml model=yolo26n.pt epochs=10 imgsz=640

# Export
yolo export model=yolo26n.pt format=onnx imgsz=640

Python example

from ultralytics import YOLO

model = YOLO("yolo26n.pt")
results = model("https://ultralytics.com/images/bus.jpg")

Current docs and resources

• Main repository: https://github.com/ultralytics/ultralytics
• YOLO26 docs: https://docs.ultralytics.com/models/yolo26
• Python usage: https://docs.ultralytics.com/usage/python
• CLI usage: https://docs.ultralytics.com/usage/cli
• Train mode: https://docs.ultralytics.com/modes/train
• Predict mode: https://docs.ultralytics.com/modes/predict
• Export mode: https://docs.ultralytics.com/modes/export
• Tracking mode: https://docs.ultralytics.com/modes/track
• Roadmap: https://www.ultralytics.com/roadmap

When should I still use this YOLOv3 repo?

Use this repository when you intentionally need the YOLOv3 codebase, historical YOLOv3 behavior, or old project compatibility.

For new models, new features, current docs, and active maintenance, start in ultralytics/ultralytics.

[UltralyticsAssistant]

👋 Hello @glenn-jocher, thank you for opening this Ultralytics YOLOv3 documentation tracking issue 🚀! This is an automated response to help organize next steps, and an Ultralytics engineer will also assist soon.
We recommend a visit to the Ultralytics YOLOv3 Docs where you can find usage examples and answers to many common questions.
If this issue expands into a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.
If this involves custom training ❓ questions or documentation gaps around training workflows, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
For documentation updates, gaps, or migration notes, sharing specific pages, unclear sections, and suggested improvements will help the team triage and improve the resources faster 📝
Join the Ultralytics community where it suits you best. For real-time chat, head to Discord 🎧. Prefer in-depth discussions? Check out the Forums. Or dive into threads on our Subreddit to share knowledge with the community.

Install

Clone the repository and install dependencies in a Python>=3.8.0 environment with PyTorch>=1.8:

git clone https://github.com/ultralytics/yolov3
cd yolov3
pip install -r requirements.txt

Status

If this badge is green, all Ultralytics YOLOv3 CI tests are passing. CI tests verify correct operation of YOLOv3 training, validation, inference, and export on every commit.