Understanding the impact of data set design on model training and performance can help alleviate the costs associated with generating remote sensing and overhead labeled data. This work examined the impact of training shifted window transformers using bounding boxes and segmentation labels, where the latter are more expensive to produce. We examined classification tasks by comparing models trained with both target and backgrounds against models trained with only target pixels, extracted by segmentation labels. For object detection models, we compared performance using either label type when training. We found that the models trained on only target pixels do not show performance improvement for classification tasks, appearing to conflate background pixels in the evaluation set with target pixels. For object detection, we found that models trained with either label type showed equivalent performance across testing. We found that bounding boxes appeared to be sufficient for tasks that did not require more complex labels, such as object segmentation. Continuing work to determine consistency of this result across data types and model architectures could potentially result in substantial savings in generating remote sensing data sets for deep learning.

本研究通过比较使用边界框和分割标签来训练平移窗口变换器的模型，对数据集设计对模型训练和性能的影响进行了研究，发现只使用目标像素训练的模型在分类任务中没有性能改善，而边界框在物体检测性能上与分割标签等价。通过确定这一结果在数据类型和模型架构上的一致性，可以在生成遥感数据集时实现可观的成本节约。

标签类型对使用航空影像训练SWIN模型的影响