Oktoberfest Food Dataset

The data was aquired during Schanzer Almfest at Ingolstadt in 2018 by IlassAG. As a part of a practical at the Data Mining and Analytics Chair by Prof. Günnemann at TUM we were given the task to count objects at checkout. Therefore we annotated the data with bounding boxes and classes to train an object detection network.

Download

You can find the dataset here

• dataset contains the train and test datasets including the labels
  • the labels can be found in files.txt (OpenCV style)
  • <filename> <number of objects> <classid1> <x1> <y1> <w1> <h1> <classid2> <x2> <y2> <w2> <h2> ...
• models contains our pretrained tensorflow models (see Preview.ipynb for an example usage)
• video_data_zipped contains the raw videos from which the dataset were extracted

Citation

If you find this work useful you may consider citing our paper

@misc{tum2019oktoberfest,
    title={Oktoberfest Food Dataset},
    author={Alexander Ziller and Julius Hansjakob and Vitalii Rusinov and Daniel Z\"ugner and Peter Vogel and Stephan G\"unnemann},
    year={2019},
    eprint={1912.05007},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Dataset Description

Data Distribution

Class Id	Class	Images	Annotations	average quantity
0	Bier	300	436	1.45
1	Bier Mass	200	299	1.50
2	Weissbier	229	298	1.30
3	Cola	165	210	1.27
4	Wasser	198	284	1.43
5	Curry-Wurst	120	159	1.32
6	Weisswein	81	105	1.30
7	A-Schorle	90	98	1.09
8	Jaegermeister	43	152	3.53
9	Pommes	110	126	1.15
10	Burger	105	122	1.16
11	Williamsbirne	50	121	2.42
12	Alm-Breze	100	114	1.14
13	Brotzeitkorb	65	72	1.11
14	Kaesespaetzle	92	100	1.09
	Total	1110	2696	2.43

Statistics

Benchmark

For training object detection models we used tensorflow's Object Detection API. We trained several different approaches and got best results for a Single Shot Detector (SSD) with Feature Pyramid Networks (FPN). Our evaluation metric was the area under the precision-recall curve on a test set of 86 images (as our goal was to count we ignored the localization).

Approach	Backbone model	Area	Example precision@recall
SSD	Mobilenet	0.86	[email protected]
SSD + FPN	Mobilenet	0.98	[email protected]
RFCN	ResNet-101	0.965	[email protected]

Code

The Evaluation folder contains Jupyter notebooks to evaluate the TensorFlow models.

With the Preview notebook one can try out the pretrained TensorFlow models on arbitrary images.

The CreateTFRecordFile notebook contains code to convert the dataset in to the TFRecord file format so it can be used with the TensorFlow object detection library.

The ShowAnnotations visualizes the bounding boxes of the dataset. Use 'n' for the next image, 'p' for the previous and 'q' to quit.

PASCAL_VOC format

This was done by Vitalii Rusinov and is further explained in his fork.

Images

Video

In addition, the labels in the PASCAL_VOC format are available in the PASCAL_VOC folder.

Online Notebooks to train Faster RCNN and Retinanet models on the dataset using Google Colaboratory are available here

Faster RCNN Pytorch

RetinaNet Tensorflow

Authors

Alexander Ziller: Student of Robotics, Cognition & Intelligence (M.Sc.) at TUM
Julius Hansjakob: Student of Informatics (M.Sc.) at TUM
Vitalii Rusinov: Student of Informatics (M.Sc.) at TUM

We also want to credit Daniel Zügner for his efforts.