YOLOmatic¶
Automated computer-vision training for YOLO26, YOLOv12, YOLO11, YOLOv10, YOLOv9, YOLOv8, YOLOX, RF-DETR, SAM 3.1, and Detectron2.
Why YOLOmatic¶
- Interactive terminal wizards generate training, fine-tuning, prediction, benchmark, augmentation, conversion, TensorBoard, and upload workflows.
- Hardware-aware configuration helps pick batch sizes, workers, devices, and runtime fallbacks for CUDA, Apple Silicon MPS, and CPU environments.
- One CLI covers Ultralytics YOLO, native RF-DETR, SAM 3.1, Detectron2, Labelbox NDJSON conversion, Roboflow upload, ClearML tracking, and benchmark reports.
Quickstart¶
uv tool install --python 3.12 yolomatic
yolomatic
For repository development:
git clone https://github.com/shahabahreini/YOLOMatic.git
cd YOLOMatic
uv sync
uv run yolomatic
Core Workflows¶
| Workflow | Command | Output |
|---|---|---|
| Configure training | uv run yolomatic |
YAML config for the selected model and dataset |
| Train | uv run yolomatic-train |
Checkpoints, logs, exports, optional Roboflow upload |
| Predict | uv run yolomatic-predict |
Annotated images or batch inference results |
| Segment with SAM | uv run yolomatic-sam |
Auto, text-prompted, or box-prompted masks |
| Benchmark | uv run yolomatic-benchmark |
HTML report with mAP, F1, rankings, UMAP |
| Convert data | uv run yolomatic-convert |
YOLO or COCO dataset from NDJSON |
Model Families¶
| Family | Tasks | Trainer |
|---|---|---|
| YOLO26 / YOLOv12 / YOLO11 / YOLOv10 / YOLOv9 / YOLOv8 / YOLOX | Detect, segment, classify, pose, OBB where supported | Ultralytics |
| RF-DETR | Detect, segment | Native RF-DETR |
| SAM 3.1 | Open-vocabulary segmentation and mask fine-tuning | HuggingFace |
| Detectron2 | Detect, segment | Detectron2 |