Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and expensive to acquire. Development of active learning methods in such fields is highly computationally expensive and time consuming which obstructs the progression of research and leads to a lack of comparability between methods. In this work, we propose and investigate a sandbox setup for rapid development and transparent evaluation of active learning in deep object detection. Our experiments with commonly used configurations of datasets and detection architectures found in the literature show that results obtained in our sandbox environment are representative of results on standard configurations. The total compute time to obtain results and assess the learning behavior can thereby be reduced by factors of up to 14 when comparing with Pascal VOC and up to 32 when comparing with BDD100k. This allows for testing and evaluating data acquisition and labeling strategies in under half a day and contributes to the transparency and development speed in the field of active learning for object detection.
翻译:积极学习是深层学习的范例,在涉及复杂认知任务的应用中尤其重要,例如,在标签难以获取且成本昂贵的物体探测方面,积极学习是特别重要的。在这类领域开发积极学习方法在计算上非常昂贵和耗时,阻碍了研究的进展,导致方法之间缺乏可比性。在这项工作中,我们提议并调查一个沙箱设置,用于快速开发和透明评价深海物体探测中的积极学习。我们用文献中发现的数据集和探测结构的通用配置进行的实验表明,我们的沙箱环境中获得的结果代表了标准配置的结果。在与帕斯卡尔VOC比较时,获得结果和评估学习行为的总计算时间可减少14倍,与BDD100k比较时可减少32倍。这样就可以在半天之内测试和评价数据采集和标签战略,并有助于在积极学习物体探测方面的透明度和发展速度。