getitune-discovering-models

# Discovering models and recipes in getitune Every trainable model in `getitune` is backed by a **recipe** YAML under `library/src/getitune/recipe/<task>/`. Recipes are self-discovering, so listing them is how you learn what you can train and what to pass to `create_engine`. Run everything from `library/`. ## List models from Python ```python from getitune.utils import list_models list_models() # all model names list_models(return_recipes=True) # full recipe YAML paths list_models(task="DETECTION") # filter by task list_models(pattern="*efficient*") # filter by name pattern list_models(task="DETECTION", return_recipes=True) # recipe paths for one task ``` Pass any returned name (or recipe path) to `create_engine(model="...", data="...")` — see `getitune-training-a-model`. ## List models from the CLI ```bash # from library/ getitune find # lists available model recipes ``` ## Tasks Task types live in `getitune.types` (`TaskType`) and organize both the model implementations and the recipe folders: - Classification: `MULTI_CLASS_CLS`, `MULTI_LABEL_CLS`, `H_LABEL_CLS` - Detection: `DETECTION`, `ROTATED_DETECTION`, `KEYPOINT_DETECTION` - Segmentation: `INSTANCE_SEGMENTATION`, `SEMANTIC_SEGMENTATION` Recipes whose name ends in `_tile` enable the tiling pipeline for large images. Each task directory also ships an `openvino_model.yaml` recipe for running pre-exported OpenVINO IR models. ## Resolving model-name ambiguity - Passing a **model name** that matches recipes under **multiple tasks** raises a `ValueError` listing the matches — pass `task=` to disambiguate (e.g. `create_engine(model="dino_v2", task="DETECTION", ...)`). - Passing a **recipe path** (`.yaml`/`.yml`) that does not exist raises `FileNotFoundError`. - Use `list_models(task="...", return_recipes=True)` to get unambiguous full recipe paths. ## Workflow 1. **List candidates**, filtering by `task=` and/or `pattern=` to narrow down. - Done when: you have a concrete model name or recipe path. 2. **Confirm the task matches your dataset** (see `getitune-preparing-datasets`). - Done when: model task and dataset annotations agree. 3. **Hand the chosen model to `create_engine`** in `getitune-training-a-model`. ## Related skills - `getitune-training-a-model` — train the model you selected. - `getitune-preparing-datasets` — match the model's task to your data. - `geti-library-dev` — when adding a new model/recipe to the library itself.