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.
|Annotation||Annotations included in the model run||Show image assets where X annotation was used at least N times|
|Predictions||Predictions uploaded to the model run||Show text assets where the model predicted X at least N times|
|Metrics||Model run metrics (auto-generated and custom)||Show image assets with specific values for precision, recall, intersection over union, etc|
|Metadata||Metadata fields uploaded by the user||Show text assets that were captured between two datetimes|
|Data row||Includes 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|
|Label||Label ID||Show all text assets where label ID contains X|
|Dataset||Dataset that the data rows belong to||Show all image assets uploaded to dataset X|
|Project||Project that the data rows are attached to||Show 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)".
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).
Updated 20 days ago