Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline

TL;DR

This paper presents an automated pipeline for creating synthetic image datasets using Stable Diffusion and YOLOv8, enabling training of car brand classifiers solely on synthetic data with promising accuracy.

Contribution

The work introduces a fully automated synthetic dataset creation pipeline for car brand classification, demonstrating the viability of training classifiers exclusively on synthetic images.

Findings

Achieved 75% classification accuracy using synthetic data.

Demonstrated the feasibility of training classifiers solely on synthetic images.

Evaluated different modes of Stable Diffusion for dataset generation.

Abstract

Recent advancements in machine learning, particularly in deep learning and object detection, have significantly improved performance in various tasks, including image classification and synthesis. However, challenges persist, particularly in acquiring labeled data that accurately represents specific use cases. In this work, we propose an automatic pipeline for generating synthetic image datasets using Stable Diffusion, an image synthesis model capable of producing highly realistic images. We leverage YOLOv8 for automatic bounding box detection and quality assessment of synthesized images. Our contributions include demonstrating the feasibility of training image classifiers solely on synthetic data, automating the image generation pipeline, and describing the computational requirements for our approach. We evaluate the usability of different modes of Stable Diffusion and achieve a classification accuracy of 75%.