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 guide.

Converting data using Supervisely Ecosystem Apps

• Convert Supervisely to YOLO v5 format. Transform images project in Supervisely (link to format) to YOLO v5 format and prepares downloadable .tar archive.

• Export to YOLOv8 format. 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.

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, sly.Dataset object, or sly.Annotation object. For each input type, you need to provide dedicated parameters (as shown in the examples below).

sly.convert.to_yolo("./sly_project", dest_dir="./result_yolo")
# or
sly.convert.to_yolo(project, dest_dir="./result_yolo")
# or
sly.convert.to_yolo(dataset, meta=project.meta, dest_dir="./result_yolo")

This converter allows you to convert a project, dataset, or a single annotation 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. The single sly.Annotation object will be converted to a list of YOLO annotation format lines.

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

• Convert a project to YOLO format:

# 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:

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

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

• Convert a single sly.Annotation object to YOLO format:

class_names = [obj_class.name for obj_class in meta.obj_classes]
ann = sly.Annotation.from_json(ann_json, meta)
yolo_lines = ann.to_yolo(class_names, task_type)