Filters

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 model performance

Model runs contain versioned snapshots of annotations, predictions, and metadata. These versions persist over time, which means that you can search model runs for values even if they're changed elsewhere in the Labelbox platform. Filters let you search model runs for specific values.

Supported attributes for search and filter

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

Attribute	Description	Examples
Annotation	Find data rows with labels that contain or do not have certain counts of annotations	Show image assets where X annotation was used at least N times.
Prediction	Predictions uploaded to the model run	Show text assets where the model predicted X at least N times.
Data row	Filter 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.
Label	Find data rows by the ID of associated labels.	Show all text assets where a label ID contains X.
Dataset	Find data rows that belong to a particular dataset.	Show all image assets uploaded to dataset X.
Project	Find data rows that are associated with a specific labeling project	Show all text assets uploaded to project X.
Metrics	Model run metrics (auto-generated and custom)	Show image assets with specific values for precision, recall, intersection over union, etc. Filter on aggregated or per prediction metrics
Metadata	Find data rows that contain a certain metadata field and/or value	Show text assets that were captured between two datetimes

Filters

You can think of filters as pyramids with layers of logical sequence. Each filter condition is a layer that limits the data rows in the view. You can think of each layer an "AND" condition in logical construct.

Within a layer, you can add additional match conditions ("OR" conditions).

The final results count reflects the data rows that match all currently specified conditions. The following screenshot shows several AND conditions, including:

Metrics where confidence is between 0.538 and 0.84.
Metrics where IoU is between 0.01 and 0.545.
Datarows with an annotation of kite or a prediction of person.

In this example, 46 data rows match all conditions.

This filter is made of 4 AND operations — This filter combines multiple different conditions.

Filtering on custom metrics per prediction basis

You can upload custom metrics to model runs and then them in model view filters. (For help uploading custom metrics, see Model Diagnostics - Custom Metrics demo [Google Colab]).

You can filter predictions by auto-generated metrics, by custom metrics, or both. This lets you create unique view, such as filtering by a specific custom metric and then refining that with values in specific ranges, as shown in the following image. You can also combine such filters with other filters to create even more tailored views.

To do this, select Metrics as your filter type and then select per prediction. Next, select the specific feature you ant to use to filter your model view. Next, add additional filter conditions to create the specific view you need.

Use the "Metrics" filter then select "per prediction".

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).

You can pick metric filtering per prediction for sorting a particular metric, or aggregated for sorting by the arithmetic mean of the metric.