IAdet: Simplest human-in-the-loop object detection
This addresses the time-consuming data annotation problem for object detection practitioners, but it is incremental as it builds on existing human-in-the-loop concepts.
This work tackles the problem of reducing annotation time in object detection by proposing a human-in-the-loop strategy called Intelligent Annotation (IA), which reduces annotation time by 25% on the PASCAL VOC dataset while providing a trained model.
This work proposes a strategy for training models while annotating data named Intelligent Annotation (IA). IA involves three modules: (1) assisted data annotation, (2) background model training, and (3) active selection of the next datapoints. Under this framework, we open-source the IAdet tool, which is specific for single-class object detection. Additionally, we devise a method for automatically evaluating such a human-in-the-loop system. For the PASCAL VOC dataset, the IAdet tool reduces the database annotation time by $25\%$ while providing a trained model for free. These results are obtained for a deliberately very simple IAdet design. As a consequence, IAdet is susceptible to multiple easy improvements, paving the way for powerful human-in-the-loop object detection systems.