  The Semmeldetector, is a machine learning application that utilizes object
detection models to detect, classify and count baked goods in images. Our
application allows commercial bakers to track unsold baked goods, which allows
them to optimize production and increase resource efficiency. We compiled a
dataset comprising 1151 images that distinguishes between 18 different types of
baked goods to train our detection models. To facilitate model training, we
used a Copy-Paste augmentation pipeline to expand our dataset. We trained the
state-of-the-art object detection model YOLOv8 on our detection task. We tested
the impact of different training data, model scale, and online image
augmentation pipelines on model performance. Our overall best performing model,
achieved an<EMAIL_ADDRESS>of 89.1% on our test set. Based on our results, we conclude
that machine learning can be a valuable tool even for unforeseen industries
like bakeries, even with very limited datasets.
