getitune-discovering-models

name

getitune-discovering-models

description

Discover which models, recipes, and tasks the getitune library (the Geti training library) supports before training. Use when a user asks what models are available, how to list recipes, how to filter by task or name pattern, how `list_models(...)` and `getitune find` behave, or how to resolve the "model name matches multiple tasks" error. Covers classification, detection, instance/semantic segmentation, and keypoint detection recipes.

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

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

# 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.