Most active learning frameworks just use model predictions to find the images where the model has the lowest confidence and ignore the image diversity aspect. E.g. the model struggles with bicycles at night. The problem with this approach is that you might end up adding many new images to your labeling pipeline that are very similar to each other.

However, with Lightly you can additionally make sure you only select images that are visually different from each other. And you always get visual feedback on the selected data in our web platform. The additional control and feedback mechanism allow you to have a more focused workflow.