Soft labels in image classification are vector representations of an image's true classification. In this paper, we investigate soft labels in the context of satellite object detection. We propose using detections as the basis for a new dataset of soft labels. Much of the effort in creating a high-quality model is gathering and annotating the training data. If we could use a model to generate a dataset for us, we could not only rapidly create datasets, but also supplement existing open-source datasets. Using a subset of the xView dataset, we train a YOLOv5 model to detect cars, planes, and ships. We then use that model to generate soft labels for the second training set which we then train and compare to the original model. We show that soft labels can be used to train a model that is almost as accurate as a model trained on the original data.

翻译：在图像分类中，软标签是图像真实分类的向量表示。本文在卫星目标检测的背景下研究了软标签，提出以检测结果为基础构建新的软标签数据集。创建高质量模型的主要工作在于收集和标注训练数据。若能利用模型自动生成数据集，不仅可快速创建数据集，还能补充现有的开源数据集。我们采用xView数据集的一个子集，训练YOLOv5模型以检测汽车、飞机和船舶。随后利用该模型为第二个训练集生成软标签，并基于此训练新模型，与原模型进行对比。实验表明，使用软标签训练的模型准确率几乎与原始数据训练的模型相当。