论文标题
从多个数据集中训练对象检测模型的伪标记阈值的非著作优化
Non-iterative optimization of pseudo-labeling thresholds for training object detection models from multiple datasets
论文作者
论文摘要
我们提出了一种非著作方法，以优化从低成本数据集的集合中学习对象检测的伪标记的阈值，每个阈值仅针对所有对象类的子集进行注释。解决这个问题的一种流行方法是首先培训教师模型，然后在训练学生模型时将其自信的预测用作伪基真实标签。但是，为了获得最佳结果，必须调整预测置信度的阈值。这个过程通常涉及迭代搜索和对学生模型的重复培训，并且很耗时。因此，我们通过在验证数据集中最大化$f_β$ -score来开发一种优化阈值而无需迭代优化的方法，该数据集在验证数据集中测量了伪标签的质量，并且可以在不训练学生模型的情况下测量。我们通过实验表明，我们提出的方法获得了与可可和VOC数据集上的网格搜索相当的地图。
We propose a non-iterative method to optimize pseudo-labeling thresholds for learning object detection from a collection of low-cost datasets, each of which is annotated for only a subset of all the object classes. A popular approach to this problem is first to train teacher models and then to use their confident predictions as pseudo ground-truth labels when training a student model. To obtain the best result, however, thresholds for prediction confidence must be adjusted. This process typically involves iterative search and repeated training of student models and is time-consuming. Therefore, we develop a method to optimize the thresholds without iterative optimization by maximizing the $F_β$-score on a validation dataset, which measures the quality of pseudo labels and can be measured without training a student model. We experimentally demonstrate that our proposed method achieves an mAP comparable to that of grid search on the COCO and VOC datasets.
