  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%.
