The growth of generative adversarial network (GAN) models has increased the ability of image processing and provides numerous industries with the technology to produce realistic image transformations. However, with the field being recently established there are new evaluation metrics that can further this research. Previous research has shown the Fr\'echet Inception Distance (FID) to be an effective metric when testing these image-to-image GANs in real-world applications. Signed Inception Distance (SID), a founded metric in 2023, expands on FID by allowing unsigned distances. This paper uses public datasets that consist of fa\c{c}ades, cityscapes, and maps within Pix2Pix and CycleGAN models. After training these models are evaluated on both inception distance metrics which measure the generating performance of the trained models. Our findings indicate that usage of the metric SID incorporates an efficient and effective metric to complement, or even exceed the ability shown using the FID for the image-to-image GANs

使用公共数据集对图像处理中的生成对抗网络（GAN）模型进行训练和评估，发现使用 SID 指标能够有效衡量模型的生成性能，超过或者与使用 FID 指标相当。

生成对抗网络中FID和SID指标的评估