Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images
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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_ | 1866909330519883776 |
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| author | Schmitt, Thomas H. Bundscherer, Maximilian Bocklet, Tobias |
| author_facet | Schmitt, Thomas H. Bundscherer, Maximilian Bocklet, Tobias |
| contents | In the food industry, reprocessing returned product is a vital step to increase resource efficiency. [SBB23] presented an AI application that automates the tracking of returned bread buns. We extend their work by creating an expanded dataset comprising 2432 images and a wider range of baked goods. To increase model robustness, we use generative models pix2pix and CycleGAN to create synthetic images. We train state-of-the-art object detection model YOLOv9 and YOLOv8 on our detection task. Our overall best-performing model achieved an average precision AP@0.5 of 90.3% on our test set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20122 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images Schmitt, Thomas H. Bundscherer, Maximilian Bocklet, Tobias Computer Vision and Pattern Recognition Machine Learning In the food industry, reprocessing returned product is a vital step to increase resource efficiency. [SBB23] presented an AI application that automates the tracking of returned bread buns. We extend their work by creating an expanded dataset comprising 2432 images and a wider range of baked goods. To increase model robustness, we use generative models pix2pix and CycleGAN to create synthetic images. We train state-of-the-art object detection model YOLOv9 and YOLOv8 on our detection task. Our overall best-performing model achieved an average precision AP@0.5 of 90.3% on our test set. |
| title | Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.20122 |