Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline
This work addresses data scarcity in image classification for specific domains like car brands, though it is incremental as it builds on existing models like Stable Diffusion and YOLOv8.
The authors tackled the problem of limited labeled data for visual car brand classification by creating an automatic pipeline using Stable Diffusion to generate synthetic images and YOLOv8 for detection and quality assessment, achieving a classification accuracy of 75% when training solely on synthetic data.
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%.