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Found 2 Skills
Prepare and point datasets at the getitune library (the Geti training library) for training, testing, and prediction. Use when a user asks which dataset formats are supported, how the `data=` argument of `create_engine(...)` / `--data_root` works, why format auto-detection fails, how to lay out COCO/YOLO/Pascal VOC/Datumaro-native data, how to use a zip archive, or how to pass an Ultralytics YOLO `data.yaml`. Covers Datumaro-based auto-detection and per-task data expectations.
Train a computer-vision model with the getitune library (the Geti training library) using its Python API or CLI. Use when a user wants to train, fine-tune, or evaluate a model with `create_engine(...)` and `engine.train()/engine.test()`, run `getitune train`/`getitune test`, pick or override a recipe under `getitune.recipe.<task>`, choose a device (cpu/gpu/xpu/cuda), warm-start from a checkpoint, or debug a training run. Covers classification, detection, instance/semantic segmentation, and keypoint detection.