###### Abstract  
Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Frechet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets. GAN dataset; car image dataset; Generative Adversarial Network; automotive image dataset; GAN neural network +
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market share. A large and high-quality car dataset is, therefore, essential in this area. Observing the existing LSUN car dataset, one can see immediately that the data are not completely pruned, and that there is room for improvement as it contains various types of noise. An example is shown in Figure 1. This is simply a consequence of using Amazon's Mechanical Turk service [5] for data annotation. Amazon Mechanical Turk is a crowdfunding marketplace that outsources the process and jobs to a distributed workforce that performs these tasks virtually. As stated in [6], it has been observed that Mechanical Turks can be extremely imprecise when working with data, and that humans are not a good choice for classifying and annotating images. This paper proposes a new LSUN-Stanford car dataset which is a union of the pruned and improved LSUN and Stanford car datasets. This new car dataset is aimed primarily for unsupervised neural network training, such as GAN training. Namely, our proposed dataset does not prescribe a division into training, validation, and testing sets, so in its original form and without modifications, it is not suitable for supervised training. It consists of an annotated and pruned LSUN car dataset [4] coupled with the annotated and pruned Stanford car dataset [7]. Our proposed dataset was constructed by filtering out the images where the car was the most salient object in the image. We achieved this by using Convolutional Neural Networks (CNNs) for object detection and cropping out the noisy parts of images, or discarding the image entirely if it was deemed unusable. Using that method, we have created a more refined car dataset intended for training GANs. The newly created dataset was then used to retrain the StyleGAN neural network with the same parameters as [1], and achieved superior Frechet Inception Distance (FID) [8] compared to the original LSUN car dataset. The main motivation for this work was to create a better and more suitable large car dataset for training GAN neural networks. This dataset is publicly available, and is accompanied with all the required programming routines for its manipulation. A scientific contribution of this research is also the demonstration that the training of GAN networks is improved significantly by using our refined LSUN-Stanford car dataset. This paper is organized as follows. Related work on existing car datasets is reviewed briefly in Section 2. Afterwards, the main highlights of the Generative Adversarial Network StyleGAN that is used in our experiments are presented in Section 3. There follows a detailed description of the creation and structure of the proposed LSUN-Stanford car dataset in Section 4.