> For the complete documentation index, see [llms.txt](https://docs.supervisely.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.supervisely.com/data-organization/operations-with-data/overview/to_yolo.md).

# Convert to YOLO

YOLO format is a popular, text-based format for different computer vision tasks, such as object detection, segmentation, and pose estimation.

For more information on how to import YOLO format data into Supervisely, see the [Import from YOLO](/import-and-export/import/supported-annotation-formats/images/yolo.md) guide.

### Converting data using Supervisely Ecosystem Apps

* [Convert Supervisely to YOLO v5 format](https://ecosystem.supervisely.com/apps/convert-supervisely-to-yolov5-format). Transform images project in Supervisely (link to format) to YOLO v5 format and prepares downloadable `.tar` archive.
* [Export to YOLOv8 format](https://ecosystem.supervisely.com/apps/export-to-yolov8). Transform datasets from the Supervisely format to the YOLOv8 segmentation format or pose estimation format.

### Converting data using Supervisely Python SDK

Easily convert your data in one line of code using the Supervisely Python SDK.

{% hint style="success" %}
`sly.convert.to_yolo()` function automatically detects the input data type and converts it to Pascal VOC format. For example, you can pass a path to a project, sly.Project object or sly.Dataset object. To convert a Dataset, you need to provide the project meta information as shown in the example below.

```python
# Project path
sly.convert.to_yolo("./sly_project", dest_dir="./result_yolo")
# Project object
sly.convert.to_yolo(project, dest_dir="./result_yolo")
# or Dataset object
sly.convert.to_yolo(dataset, dest_dir="./result_yolo", meta=project.meta)
```

{% endhint %}

This converter allows you to convert a project or dataset to YOLO format for **detection**, **segmentation**, and **pose estimation** tasks.

Project and dataset conversion works similarly and will convert all data in the same structure to YOLO format.

{% hint style="info" %}
It supports the following geometry types:

* **detection**: `sly.Rectangle`, `sly.Bitmap`, `sly.Polygon`, `sly.GraphNodes`, `sly.Polyline`, `sly.AlphaMask`

* **segmentation**: `sly.Polygon`, `sly.Bitmap`, `sly.AlphaMask`

* **pose estimation**: `sly.GraphNodes`
  {% endhint %}

* Convert a project to YOLO format:

```python
# One line of code
sly.convert.to_yolo("./sly_project", dest_dir="./result_yolo")

# Or using the sly.Project object
project_fs = sly.Project("./sly_project", sly.OpenMode.READ)
project_fs.to_yolo("./result_yolo", task_type="segmentation")
```

* Convert a specific dataset to YOLO format:

```python
ds = project_fs.datasets.get("dataset_name")

sly.convert.to_yolo(ds, dest_dir="./result_yolo", meta=project_fs.meta)
# Or using the sly.Dataset object
ds.to_yolo(project_fs.meta, dest_dir="./result_yolo")
```