getitune-discovering-models

Original：🇺🇸 English

Translated

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.

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Sourceopen-edge-platform/geti

Added on2026-08-20

NPX Install

npx skill4agent add open-edge-platform/geti getitune-discovering-models

SKILL.md Content

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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

List candidates, filtering by
```
task=
```
and/or
```
pattern=
```
to narrow down.
- Done when: you have a concrete model name or recipe path.
Confirm the task matches your dataset (see
```
getitune-preparing-datasets
```
).
- Done when: model task and dataset annotations agree.
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.