Oktoberfest Food Dataset
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Nov 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 3.
5 papers.
- 2021Large Scale Visual Food Recognitioncited by 75
- 2022Rethinking Few-Shot Object Detection on a Multi-Domain Benchmarkcited by 17
- 2024Cross-domain Multi-modal Few-shot Object Detection via Rich Textcited by 5
- 2024Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detectioncited by 3
- 2024Beyond Few-shot Object Detection: A Detailed Surveycited by 2
- 2024Beyond Few-shot Object Detection: A Detailed Survey26 Aug 2024
- 2024Cross-domain Multi-modal Few-shot Object Detection via Rich Text24 Mar 2024
- 2024Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection14 Feb 2024
- 2022Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark22 Jul 2022
- 2021Large Scale Visual Food Recognition30 Mar 2021