## 6 Results  
In our experiments, the StyleGAN network was implemented using the CUDA 10.0 library and Tensorflow 1.15.0, while PyTorch 1.2 was employed for the MMDetection toolkit. The object detection using MMDetection toolbox was performed on a computer with one Nvidia 1080 Ti graphics card. The estimated object detection speed using MMDetection toolbox is 10.9 frames per second [24]. However, with image preprocessing and postprocessing, the effective object detection speed was slightly lower in our experiments, i.e., around 10 frames per second. With approximately 5.5 million images in the LSUN and the Standford car datasets, a simple calculation points out that the object detection for both datasets took approximately six days and nine hours of processing time. Our implementation of the StyleGAN network was trained on a computer with two NVIDIA 1080 Ti GPUs, an AMD 1950X processor, and 32 GB of RAM. The StyleGAN was trained by 46 million images. This training phase was completed in around 46 days and nine hours on our hardware. Identically to [PERSON] et al. [1], we utilized the Frechet Inception Distance (FID) to assess the quality and efficiency of a trained GAN network. The Inception v3 neural network model [28] was utilized to calculate FID. Specifically, the last pooling layer prior to the output classification of images was used to capture computer vision-specific features of an output image. These activations were calculated for a collection of images. FID [8] is much more consistent than inception score [6] at estimating the  
Figure 7: LSUN-Stanford car dataset: Images with appropriate bounding boxes. distance between a real and a generated image. Namely, FID applies real-world sample images for the comparison with synthetic images, unlike inception score, which uses only fake images to assess the quality of generated images. The same evaluation protocol was employed as in [PERSON] et al. [1]. We calculated the FIDs using 50,000 images drawn randomly from the LSUN-Standford dataset, and reported the lowest FID metrics during the training (all other images from this dataset were employed for the training). The number of images used for FID calculation is a parameter that can be set arbitrarily. However, it should be chosen appropriately to have FID values be as precise as possible, and such that the FID calculation would not take an extremely large amount of processing time. The calculation of the FID metric on 50,000 images was considered to be representative, because it was verified that the so-calculated FID metrics do not differ significantly from the FID metrics calculated on the entire dataset [6; 29]. Figure 8 depicts how the FID metrics were changing during training of our StyleGAN network. The final FID metric obtained after completing the training of our StyleGAN neural network on the pruned LSUN-Stanford Car Dataset was 3.15. For comparison, let us summarise the result if the SyleGAN neural network is trained just on the original LSUN car dataset. The calculated FID equalled 3.27 in this case [1]. Lower FID means better results. Around 3.7% improvement was observed when using the proposed LSUN-Standford car dataset for the StyleGAN training. It should be stressed that the same training protocol and StyleGAN hyperparameters were used in both experiments. Let us also present some qualitative results. Figure 9 depicts generated images obtained by the generator network of our trained StyleGAN network. The visual quality is also exceptional for other generated images. For non-experts regarding cars, it is almost impossible to pinpoint the obvious flaws in generated cars. The process of our StyleGAN network training and generating cars for some sample images after each training epoch is demonstrated on the link [[https://youtu.be/NCuJ](https://youtu.be/NCuJ)]([https://youtu.be/NCuJ](https://youtu.be/NCuJ)) Ada7 Qus. For comparison, we can inspect the generated cars in Figure 10. These images were generated by the StyleGAN, trained just with the original LSUN car dataset.