getitune-preparing-datasets

Original：🇺🇸 English

Translated

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.

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Sourceopen-edge-platform/geti

Added on2026-08-20

NPX Install

npx skill4agent add open-edge-platform/geti getitune-preparing-datasets

SKILL.md Content

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Preparing datasets for getitune

When you pass a filesystem path to

data=

(Python API) or

--data_root

(CLI),

getitune

uses Datumaro to auto-detect the dataset format — you point at the dataset root and the same call works regardless of the underlying format.

Run everything from

library/

.

Supported formats and how they are detected

Format	Detected by
COCO	an `annotations/` directory with COCO JSON files
YOLO	a `data.yaml` file (Ultralytics layout)
Pascal VOC	`JPEGImages/` , `Annotations/` , `ImageSets/` directories
Datumaro (native)	`metadata.json` + `data.parquet` at the root

Zip archives are accepted too; Datumaro extracts them on import.
Point
```
data=
```
at the dataset root — the directory that directly contains the marker files/folders above, not a parent of it.

Workflow

python

from getitune.engine import create_engine

# Same call for any supported format — just point at the root
engine = create_engine(
    model="src/getitune/recipe/detection/yolox_s.yaml",
    data="/path/to/dataset_root",
)
engine.train()

Lay the dataset out as one supported format and confirm the marker files/folders sit at the root you will pass.
- Done when: the root matches exactly one row in the table above.
Match the dataset to the task. A detection dataset needs bounding-box annotations; segmentation needs masks; classification needs per-image labels. Task and labels must agree with the model you choose in the
```
getitune-training-a-model
```
skill.
- Done when:
```
create_engine(...)
```
  builds a datamodule without a feature/label mismatch error.
Smoke-test loading with a tiny run (
```
engine.train(max_epochs=1)
```
) before a full run.
- Done when: one train + one validation batch load without shape errors.

Ultralytics YOLO datasets

If you train an Ultralytics YOLO model, pass the Ultralytics

data.yaml

file directly as

data=

(or

--data_root

). Ultralytics support requires an install from source with the

ultralytics

extra (it is not in the PyPI package).

Debugging auto-detection

Wrong/failed format detection: the root probably has extra nesting or a missing marker. Verify the exact marker files (
```
annotations/
```
for COCO,
```
data.yaml
```
for YOLO, the three VOC dirs,
```
metadata.json
```
+
```
data.parquet
```
for native) are directly under the path you pass.
Feature/label mismatch during training: the dataset's annotation type does not match the task — cross-check with
```
getitune-training-a-model
```
and pass an explicit
```
task=
```
.
Backend dataset conversion: the Geti application converts Geti-internal datasets to/from COCO/VOC via
```
application/backend/app/datumaro_converter/
```
; that is a separate, app-side path from library
```
data=
```
usage.

Related skills

```
getitune-training-a-model
```
— consumes the prepared dataset via
```
data=
```
.
```
getitune-discovering-models
```
— pick a model that matches the dataset's task.