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：图像分类中的软标签是图像真实分类的矢量表示 。 在本文中, 我们调查卫星物体探测背景下的软标签 。 我们建议使用检测作为软标签新数据集的基础 。 创建高质量模型的大部分努力正在收集和说明培训数据 。 如果我们能够使用模型为我们生成数据集, 我们不仅可以快速创建数据集, 还可以补充现有的开放源数据集 。 我们使用 xVView 数据集的一个子集, 我们训练一个 YOLOv5 模型来检测汽车、 飞机和船只 。 然后我们用该模型为第二个培训组生成软标签, 然后我们培训和比较原始模型。 我们显示软标签可以用来培训一个几乎与原始数据培训模型一样准确的模型 。