Using search and filters


Supported attributes for search and filter

Here are the supported search and filter capabilities in Catalog.





Filter on annotations created on or uploaded to Labelbox

Show images where X was annotated

Predictions (coming soon)

Filter on predictions uploaded to model runs

Show images where a model detected X


Filter to the Dataset data rows were created in

Show all images uploaded to dataset X


Custom metadata fields uploaded by the user

The datetime an image was captured

Project status

Status with respect to project

Data Rows not submitted to a particular project

Similarity functions

Filter by a function score

Use similarity to find data for labeling

Media attributes

Attributes of the data computed on upload. Each Media Type has different fields.

Media Type: Image, Video, Text,...
Video duration

Constructing a filter

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 or annotations that match the filter. Only non-zero counts of instance 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 vessels on synthetic aperture radar satellite imagery. 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)".


Then, the engineer queries for images that are more similar to the images used to create a function named "coastal images". The results are images with a coastline.


The engineer then tunes the function parameter that results in images that are dissimilar to the images used to create a function named "coastal images". The results are images without any coastline.


Then, the engineer queries for images that are not labeled (does not contain annotation named "ship").


Sample 100 random images from the results and submit the batch to the labeling project in Annotate

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