- 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
Supported attributes for search and filter
Below are the attributes you can search and filter by in the Model product.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 as an “AND” condition in a 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. For example, a view can combine several AND conditions:- Metrics where confidence is between 0.538 and 0.84.
- Metrics where IoU is between 0.01 and 0.545.
- Data rows with an annotation of
kiteor a prediction ofperson.