Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913375205720064 |
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| author | Lippemeier, Jan Hittmeyer, Stefanie Niehörster, Oliver Lange-Hegermann, Markus |
| author_facet | Lippemeier, Jan Hittmeyer, Stefanie Niehörster, Oliver Lange-Hegermann, Markus |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01071 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline Lippemeier, Jan Hittmeyer, Stefanie Niehörster, Oliver Lange-Hegermann, Markus Computer Vision and Pattern Recognition Machine Learning 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%. |
| title | Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.01071 |