Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images
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Abstract (arXiv)
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 [email protected] of 90.3% on our test set.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 30 Sep 2024.
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References · 12
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- 2021GAN-based Data Augmentation for Chest X-ray Classificationcited by 18
- 2024Semmeldetector: Application of Machine Learning in Commercial Bakeriescited by 2
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