Semmeldetector: Application of Machine Learning in Commercial Bakeries
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911908421959680 |
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| author | Schmitt, Thomas H. Bundscherer, Maximilian Bocklet, Tobias |
| author_facet | Schmitt, Thomas H. Bundscherer, Maximilian Bocklet, Tobias |
| contents | 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 AP@0.5 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04050 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semmeldetector: Application of Machine Learning in Commercial Bakeries Schmitt, Thomas H. Bundscherer, Maximilian Bocklet, Tobias Computer Vision and Pattern Recognition 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 AP@0.5 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. |
| title | Semmeldetector: Application of Machine Learning in Commercial Bakeries |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.04050 |