Organize your data into datasets
Datasets are a way to organize your assets and labels that you have collected for labeling. After labeling, they can be exported in a variety of formats to be used in Machine Learning projects or shared with others. Plainsight web users will soon be able to train models from datasets with one click using SmartML. This feature is currently available for private cloud installations only.
Since labeled data is exported by dataset, we recommend making different datasets for different machine learning objectives. For instance, you may have one dataset for detecting boats in images, and another for reading chess boards.
To view and create datasets, click Datasets in the side nav under Label & Train
- 1.To open an existing dataset click on its preview card.
- 2.To navigate back to the dataset list from any page click Datasets tab.
- 1.To create a new dataset, click on the “Create Dataset” button near the top right corner.
- 2.Enter Name the project and click "Save & Continue to Sources"
- 3.You will be taken to the Sources tab. From here you can add your data sources
- 1.To delete a dataset, navigate to the dataset you want to delete.
- 2.Click the 3 dots at the top right.
- 3.Select "Delete Dataset" from the menu.
- 4.Click "Delete Dataset" to confirm the deletion.
You can only delete datasets that have not been used to train a model.