Oktoberfest Food Dataset
Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Z\"ugner,, Peter Vogel, Stephan G\"unnemann

TL;DR
This paper introduces the Oktoberfest Food Dataset, a comprehensive collection of annotated images and unlabeled footage from a German beer tent, aimed at advancing object detection for food and drink items.
Contribution
It provides a new, challenging dataset with annotations and trained models for object detection, specifically tailored for food and drink items in a festive setting.
Findings
Over 2,500 annotated objects across 15 categories
600GB of unlabeled video footage available
Trained models provided as benchmarks
Abstract
We release a realistic, diverse, and challenging dataset for object detection on images. The data was recorded at a beer tent in Germany and consists of 15 different categories of food and drink items. We created more than 2,500 object annotations by hand for 1,110 images captured by a video camera above the checkout. We further make available the remaining 600GB of (unlabeled) data containing days of footage. Additionally, we provide our trained models as a benchmark. Possible applications include automated checkout systems which could significantly speed up the process.
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Code & Models
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
