IAdet: Simplest human-in-the-loop object detection
Franco Marchesoni-Acland Gabriele Facciolo
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.