Copy images and their annotations from a Supervisely project, dataset or collection into an OvalBee dataset that any other template can start from.
Overview
- Task: Data import
- Input: A Supervisely project, dataset or collection and your Supervisely credentials
- Pipeline: import the images from Supervisely → collect them in an OvalBee dataset
- Output: An OvalBee dataset with the imported images and their annotations
When you need it
When your data already lives in Supervisely and you want to run it through OvalBee pipelines — training, evaluation, labeling — without exporting archives by hand.
What you need
- The URL of a Supervisely project, dataset or collection, or a project ID on its own.
- Your Supervisely server address and API token, stored once as a secret.
- A destination dataset — pick an existing one or type a new name to create it.
How it works
| Step | What happens |
|---|---|
| Import | The images and their annotations are copied from Supervisely into the destination dataset |
| Dataset | The same dataset, ready to be picked by any other template |
Annotations are imported by default; turn them off to copy image files only, or keep only the images that carry labels. Images that came from OvalBee in an earlier export are attached back to their original asset instead of being uploaded again. Every imported image is tagged with the Supervisely project, dataset or collection it came from, unless you set a tag of your own.
What an import gives you
A new version of the destination dataset on every run, so earlier imports stay available. The node also keeps a short-lived dataset with just the images handled in its last run.
Related templates
- Detector evaluation — score a detector against the imported labeled set
- Labeling jobs in Supervisely — send the images back to Supervisely as labeling jobs
- Import images by drag & drop — upload images from your computer instead