How to perform model error analysis and address model errors.
Filter and sort to keep the largest disagreements between model predictions and ground truth annotations.
basketball_court
instead of a ground_track_field
. You found a pattern of model failures that can be described as follows: “The model seems to struggle to distinguish ground track fields and basketball courts, especially when they have green and brown colors”.
The detail view helps you inspect disagreements. The goal is to find patterns of model failures.
The detail view helps you inspect disagreements. The goal is to find patterns of model failures.
basketball_court
annotation or a ground_track_field
annotation and have low IOU. This surfaces many examples of the exact edge case we discovered above: “The model struggles to distinguish ground track fields and basketball courts, especially when they have green and brown colors”.Many ground track fields and basketball courts are being mispredicted (low IOU). This is a pattern of model failure.
Labelbox helps you find patterns of model failures. In this case, the model struggles to distinguish ground track fields and basketball courts, especially when they have green and brown colors.
miscellaneous
annotation and that have low IOU. This surfaces many examples of the exact edge case we discovered above: “The model struggles to make accurate NER predictions on scientific concepts”.
Labelbox helps you find patterns of model failures. In this case, struggles to make accurate NER predictions on scientific concepts.
basketball_court
ground truth annotations. Then, you can click on the histogram bar corresponding to basketball courts and the gallery view will open. with filtering and sorting activated to show data rows in the basketball_court
bar of this histogram.
This is an alternative to steps 1-4 described above in the Use filters in the gallery view section.
The metrics view is a good way to identify classes on which the model is struggling.
ground_track_field
annotation and those containing a basketball_court
annotation overlap. The two classes are not easy to separate in the embedding space. This is an indicator that the model is likely to struggle with the data rows at the intersection of the two clusters.
Use the projector view to identify intersections of clusters where the model may be struggling.
basketball_court
cluster and the ground_track_field
cluster. Once the data rows are selected, you can switch back to the grid view, and inspect these data rows.
This is an alternative approach to steps 1-4 described above in the Use filters in the gallery view section.