Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images
This work provides an incremental improvement for commercial bakeries seeking to automate the tracking of returned baked goods to increase resource efficiency.
This paper tackles the problem of automating the tracking of returned baked goods in commercial bakeries. The authors created an expanded dataset and used generative models to create synthetic images, achieving an average precision AP@0.5 of 90.3% with YOLOv9.
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.