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This work proposes a strategy for training models while annotating data namedIntelligent Annotation (IA). IA involves three modules: (1) assisted dataannotation, (2) background model training, and (3) active selection of the nextdatapoints. Under this framework, we open-source the IAdet tool, which isspecific for single-class object detection. Additionally, we devise a methodfor automatically evaluating such a human-in-the-loop system. For the PASCALVOC dataset, the IAdet tool reduces the database annotation time by $25\%$while providing a trained model for free. These results are obtained for adeliberately very simple IAdet design. As a consequence, IAdet is susceptibleto multiple easy improvements, paving the way for powerful human-in-the-loopobject detection systems.

IAdet: Simplest human-in-the-loop object detection