Drop images from your computer onto the graph and get an OvalBee dataset that every other template can start from.
Overview
- Task: Data import
- Input: Image files on your computer
- Pipeline: drop files onto the import node → collect them in a dataset
- Output: An OvalBee dataset holding the uploaded images
When you need it
Every template in the catalog starts from a dataset that is already in OvalBee. When your images sit in a local folder, this is the shortest way to get them there: no storage to connect, no archive to prepare, no link to share.
What you need
A folder of images and a name for the destination dataset — pick an existing one or type a new name to create it.
How it works
| Step | What happens |
|---|---|
| Upload | You drop files onto the import node and they are added to the destination dataset |
| Dataset | The same dataset, ready to be picked by any other template |
Each drop adds a new version of the dataset, so what it held before stays available. When a dropped file has the same folder and name as an image already there, the node skips it by default; switch it to replace the old image or to keep both under a free name.
What an import gives you
The destination dataset with every uploaded image in it, plus a badge on the node with how many files the last drop brought in. Drop more files later and they join the same dataset as its next version.
Related templates
- Import from Dataset Ninja — bring in a public dataset by its link instead of uploading files
- Import from Supervisely — copy images and annotations from a Supervisely project
- Labeling with review loop — label the imported images and review every annotation