Explore the filters available for curating data in model runs.

Labelbox lets you slice and dice your model predictions, annotations, and metrics. Powerful filtering and sorting make it simpler and more efficient to:

  • Analyze the performance of neural networks
  • Find low-performing slices of data
  • Surface labeling mistakes
  • Identify high-impact data to label in order to maximally improve neural network performance


A model run contains a snapshot of annotations, predictions, and metadata

The annotations, predictions and metadata in a model run are versioned. Even if you update these annotations, predictions, and metadata elsewhere in the Labelbox platform, they will remain intact in the model run.

Supported attributes for search and filter

Below are the attributes you can search and filter by in the Model product.

AnnotationFind data rows with labels that contain or do not have certain counts of annotationsShow image assets where X annotation was used at least N times.
PredictionPredictions uploaded to the model runShow text assets where the model predicted X at least N times.
Data rowFilter on any data row identifiers (e.g., global key, data row ID, and external ID)Show all image assets where the global key does not contain X.
LabelFind data rows by the ID of associated labels.Show all text assets where a label ID contains X.
DatasetFind data rows that belong to a particular dataset.Show all image assets uploaded to dataset X.
ProjectFind data rows that are associated with a specific labeling projectShow all text assets uploaded to project X.
MetricsModel run metrics (auto-generated and custom)Show image assets with specific values for precision, recall, intersection over union, etc
MetadataFind data rows that contain a certain metadata field and/or valueShow text assets that were captured between two datetimes


You can think of creating a filter like constructing a pyramid with layers of logical sequence. Each layer is an AND operation. Within a layer, you can use OR operations. Each filter provides a count of data rows that match the filter. Only non-zero counts of instances of an attribute are available for selection.

This filter is made of 4 AND operations

This filter is made up of 4 AND operations.

Here is a realistic example to help you understand filter construction: An ML engineer is developing an AI model to identify people on an object detection dataset. The engineer learns that the model performs poorly on images containing coastlines. So the engineer queries for images that are at least 200px wide AND belong to the dataset named "SAR dataset (chipped)".

Sort by

You can sort data on any combination of auto-generated metrics and/or custom metrics.

To do so, click on Sort by, then pick the desired metric and decide if the sorting should be in ascending order (Asc) or descending order (Desc).