Filtering and sorting

Labelbox enables you to 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

Supported attributes for search and filter

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

AnnotationAnnotations included in the model runShow image assets where X annotation was used at least N times
PredictionsPredictions uploaded to the model runShow text assets where the model predicted X at least N times
MetricsModel run metrics (auto-generated and custom)Show image assets with specific values for precision, recall, intersection over union, etc
MetadataMetadata fields uploaded by the userShow text assets that were captured between two datetimes
Data rowIncludes any data row identifiers (e.g., data row IDs, global keys, and external IDs)Show all image assets where the global key does not contain X
LabelLabel IDShow all text assets where label ID contains X
DatasetDataset that the data rows belong toShow all image assets uploaded to dataset X
ProjectProject that the data rows are attached toShow all text assets uploaded to project X


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.


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 and provided as a hint.

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)".

This filter is made of 4 AND operations

This filter is made of 4 AND operations


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

To do so, click on Sort by, then pick the metric you care about, then decide if the sorting should be in ascending order (Asc) or descending order (Des).