Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images
<|reference_start|>Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images: 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.<|reference_end|>
@article{schmitt2024training,
    title={Training a Computer Vision Model for Commercial Bakeries with Primarily
  Synthetic Images},
    author={Thomas H. Schmitt, Maximilian Bundscherer, Tobias Bocklet},
    journal={arXiv preprint arXiv:2409.20122},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20122},
    primaryClass={cs.CV cs.LG}
}