How to upload predictions on geospatial data in a model run and sample upload formats.
To upload predictions in Labelbox, you need to create the prediction payload. This section provides this payload for every prediction type.
Labelbox supports two formats for the predictions payload:
Both are described below.
If you do not specify a confidence score, the prediction will be treated as if it had a confidence score of 1.
Follow the steps below to upload predictions to a model run.
You will need to import these libraries to use the code examples in this section.
Replace the value of API_KEY
with a valid API key to connect to the Labelbox client.
Your model run should have the correct ontology set up with all the tools and classifications supported for your predictions.
Here is an example of creating an ontology programmatically for all the example predictions above:
Create the annotations payload using the snippets of code shown above.
Labelbox supports two formats for the annotations payload: NDJSON and Python annotation types. Both approaches are described below with instructions to compose annotations into Labels attached to the data rows.
The resulting labels
and ndjson_labels
from each approach will include every annotation (created above) supported by the respective method.