Bring data into OvalBee
Every flow starts with a dataset. There are three ways to fill one, each a single node with a matching template in the library.
All three templates have the same shape: the import node, followed by a Dataset node that points at the result and gives the rest of your flow one place to reference it.
Upload files from your computer
Set the node’s Destination dataset (type a new name to create one) and what should happen On name conflict — skip the new file, replace the old one, or keep both under a new name. Then drop files or whole folders onto the node, or click it to pick them. The node runs by itself as soon as the upload finishes.
- Each upload adds a new version of the destination dataset, so the state before the upload stays available.
- Images and videos are imported. Annotation files are skipped — to bring existing labels along, import from Supervisely or Dataset Ninja.
- The node’s badge shows how many files the last upload imported and skipped.
Set the inputs before dropping files: the run starts the moment the upload completes and uses whatever the inputs hold at that point.
Pull a public dataset from Dataset Ninja
Dataset Ninja is a catalog of public computer vision datasets. In the node, open the dataset picker, search the catalog by name or category, and choose Full or the smaller Sample under Import size. You can also paste a link to a dataset page.
Images arrive with their annotations, and each image is tagged with the split it came from - train, val, test - and with its image-level labels. That lets a Filter images node or a train/validation split reuse the original split instead of making a new one. The classes of the imported dataset are added to the destination’s ontology.
This is the fastest way to try any flow in OvalBee without preparing data first, and what the Quickstart uses.
Running the Dataset Ninja import again imports every image again, next to the ones already there. Point it at a new dataset if you want a fresh copy.
Import from Supervisely
Store your Supervisely address and API token as a Supervisely secret under Settings → Secrets (see Secrets and API tokens), then paste the URL of a Supervisely project, dataset or collection into the node, or just a project ID.
- Choose whether to bring annotations along, and whether to import only images that are already labeled.
- Each run adds a new version of the destination dataset, and the project’s classes are added to its ontology.
- Images that were exported from OvalBee to Supervisely earlier can be attached back to their original assets instead of being uploaded twice.
If your team keeps labeling in Supervisely, you don’t have to import at all: the Supervisely labeling nodes run the queue there and mirror its progress into OvalBee. See Organize labeling.
Other sources
Importing straight from your own cloud buckets isn’t available as a node in the private beta. If your data lives in S3, Azure or another system, tell your OvalBee contact — connecting your source is part of onboarding. Models trained elsewhere come in through the Upload model node.
Next steps
- Check and clean a dataset — profile what you just imported.
- Organize labeling — label it.
- Try a pretrained detector — see what a model already finds in it.