Upload custom metrics
If the auto-generated metrics are not sufficient for your use case, you can upload custom metrics to your model run. This will help you even more precisely evaluate your model performance in Labelbox.Upload custom metrics to individual prediction annotations.
To upload custom metrics to individual predictions, you can append the following list of dictionaries to the respective prediction. The custom metric fields are supported for all annotation types except raster segmentation.Scalar custom metrics
AScalarMetric
is a custom metric with a single scalar value. It can be uploaded at the following levels of granularity:
Data rows
Features
Nested features
Aggregation of custom metrics
This is an optional field on theScalarMetric
object, to control how custom metrics are aggergated. By default, the aggregation uses ARITHMETIC_MEAN
.
Aggregations occur in the following cases:
- When you provide a feature or nested-feature metric, Labelbox automatically aggregates the metric across features and nested-features on the data row. For example, say you provide a custom metric Bounding Box Width (BBW) on the features “cat” and “dog” . The data row-level metric for BBW is the average of these two values.
- When you create slices, the custom metric is aggregated across data rows of the Slice.
- When you filter data inside a Model Run, the custom metric is aggregated across the filtered data rows.