YOLOmatic is a production-focused Python CLI/TUI for configuring, training, fine-tuning, predicting, benchmarking, augmenting, converting, monitoring, and uploading computer-vision models. It covers YOLO26/12/11/10/9/8, YOLOX, RF-DETR, SAM 3.1, and Detectron2 from one terminal workflow.

Table of Contents

• Why YOLOmatic
• 30-Second Quickstart
• Feature Matrix
• FAQ
• Documentation
• Versioning

Why YOLOmatic

• Interactive wizard UX: configure models, datasets, fine-tuning, prediction, benchmarking, augmentation, NDJSON conversion, TensorBoard, and upload flows without hand-writing boilerplate.
• Hardware-aware configs: CUDA, Apple Silicon MPS, CPU fallback, workers, batch sizes, and runtime preflight checks are handled before training starts.
• Safe offline augmentation: isolated worker processes create reproducible Albumentations datasets without replacing a prior output until the new one succeeds.
• 10+ model families in one CLI: Ultralytics YOLO, native RF-DETR, SAM 3.1, Detectron2, Roboflow upload, ClearML tracking, Labelbox conversion, and local benchmark reports.

30-Second Quickstart

uv tool install --python 3.12 yolomatic
yolomatic

Repository development:

git clone https://github.com/shahabahreini/YOLOMatic.git
cd YOLOMatic
uv sync
uv run yolomatic

Common commands:

uv run yolomatic-train
uv run yolomatic-predict
uv run yolomatic-benchmark
uv run yolomatic-convert
uv run yolomatic-upload
uv run yolomatic-tensorboard

Versioning

Use the built-in semantic version command before creating a release tag:

uv run bump patch  # or: minor / major
git tag vX.Y.Z
git push origin vX.Y.Z

Release tags must point to a commit already on main; GitHub Actions verifies the tag and package version before publishing the wheel and source archive.

Feature Matrix

Comparison

FAQ

What is YOLOmatic?

YOLOmatic is a Python 3.12 command-line toolkit with an interactive terminal UI for computer-vision training workflows.

Which model families are supported?

YOLO26, YOLOv12, YOLO11, YOLOv10, YOLOv9, YOLOv8, YOLOX, RF-DETR, SAM 3.1, and Detectron2.

Is YOLOmatic only for YOLO?

No. YOLOmatic also supports native RF-DETR training and fine-tuning, SAM 3.1 segmentation workflows, and Detectron2 training.

Does it require a GPU?

No. CUDA is recommended for training, but CPU and Apple Silicon MPS fallbacks are supported. YOLOmatic detects common CUDA/PyTorch mismatches and offers repair guidance.

Can YOLOmatic convert Labelbox NDJSON?

Yes. It converts Labelbox and Ultralytics-platform NDJSON exports into YOLO or COCO datasets with concurrent image downloads. Ultralytics pose exports can be converted explicitly to YOLO Pose or COCO Pose while preserving keypoints.

Can I benchmark trained models?

Yes. uv run yolomatic-benchmark compares Ultralytics YOLO checkpoints and exports such as .pt, ONNX, TensorRT .engine, TorchScript, and OpenVINO, then verifies model/dataset task compatibility and evaluates train, valid, test, or all groups. Detection and instance segmentation report mAP/F1, pose reports box metrics, and semantic segmentation reports mIoU, pixel accuracy, and Dice.

Can I export models for deployment?

Yes. uv run yolomatic-export exports trained YOLO .pt weights to TensorRT, ONNX, OpenVINO, CoreML, TFLite, TorchScript, NCNN, RKNN, and other Ultralytics formats. TensorRT workspace is user-controlled so it can be tuned for the GPU that builds the engine. The export wizard reads the selected checkpoint details first, then hides options that do not apply to that model task or export format.

Can I upload models to Roboflow?

Yes. YOLOmatic uploads YOLO checkpoints and deploys RF-DETR checkpoints through the upload TUI or optional post-training upload config.

Screenshots

Links

• Full documentation
• Install guide
• Models guide
• YOLO guide
• Dataset guide
• Comparison
• Changelog
• Contributing
• Citation

Citation

@software{bahreini_jangjoo_yolomatic_2026,
  author = {Bahreini Jangjoo, Shahab},
  title = {{YOLOmatic}: automated computer-vision training CLI for YOLO, RF-DETR, SAM, and Detectron2},
  year = {2026},
  version = {4.4.0},
  license = {Apache-2.0},
  url = {https://github.com/shahabahreini/YOLOMatic},
  note = {Python CLI/TUI for configuring, training, fine-tuning, predicting, benchmarking, and uploading computer-vision models}
}

License

YOLOmatic is licensed under Apache 2.0.

Acknowledgments

YOLOmatic builds on the Python computer-vision ecosystem, including Ultralytics, RF-DETR, SAM/HuggingFace, Detectron2, PyTorch, Albumentations, ClearML, TensorBoard, Roboflow, and MkDocs Material.