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.

翻译：本文提出了一种名为智能标注（Intelligent Annotation, IA）的边标注边训练策略。IA包含三个模块：（1）辅助数据标注，（2）背景模型训练，（3）主动选择下一数据点。在此框架下，我们开源了专为单类目标检测设计的IAdet工具。此外，我们还提出了一种自动评估此类人机协同系统的方法。在PASCAL VOC数据集上，IAdet工具将数据库标注时间减少了$25\%$，同时免费提供训练好的模型。这些结果是在刻意设计的极其简单的IAdet方案下获得的。因此，IAdet具有多种易于改进的潜力，为构建强大的人机协同目标检测系统铺平了道路。