Summary of Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline, By Jan Lippemeier et al.
Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline
by Jan Lippemeier, Stefanie Hittmeyer, Oliver Niehörster, Markus Lange-Hegermann
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an automatic pipeline to generate synthetic image datasets using the Stable Diffusion model for image synthesis. The pipeline leverages YOLOv8 for bounding box detection and quality assessment. The authors demonstrate the feasibility of training image classifiers solely on synthetic data, automate the image generation process, and describe the computational requirements. They evaluate different modes of Stable Diffusion, achieving a classification accuracy of 75%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making artificial intelligence (AI) better at learning from images. Right now, AI models need lots of labeled images to learn, but it’s hard to get those labels. The authors came up with a way to create fake image datasets using an AI model called Stable Diffusion. They also developed a tool to check the quality of these fake images and make sure they’re good enough to train AI models. The goal is to make AI more accurate and efficient, and this paper shows how it’s possible to do that. |
Keywords
» Artificial intelligence » Bounding box » Classification » Diffusion » Diffusion model » Image generation » Image synthesis » Synthetic data
