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Auto-ID Labs

Object Detection Datasets in the Grocery Product Domain

This is a collection of datasets which are useful for object detection in the domain of grocery product detection. The credits for the respective datasets belong to the authors, which are credited with each dataset individually. These datasets were accumulated by the Auto-ID Labs at the ETH Zürich within an ongoing effort to accelerate computer vision research in the grocery domain. The datasets were accumulated by Klaus Fuchs & Tobias Grundmann.

Datasets

Name # Classes # Instances # Images
Holoselecta 109 10035 295
Grozi-3.2K 3235 3235 + 680
Grozi120 120 720 + 4973
SKU110K 110,712 ~1.74 * 10^6 11,762

... if you know further datasets let us know by issuing a request ...

Holoselecta

Please cite the paper as follows:
Klaus Fuchs, Tobias Grundmann, Elgar Fleisch. "Towards Identification of Packaged Products via Computer Vision." Proceedings of the IoT 2019. 2019

License: Creative Commons CC BY 4.0

A dataset of labeled bounding boxes on realistic images of products in vending machines of the Zurich area.

Download (will forward to Google Drive)

GroZi120

Please cite the paper as follows:
Michele Merler, Carolina Galleguillos, and Serge Belongie. "Recognizing groceries in situ using in vitro training data". Computer Vision and Pattern Recognition, 2007.

A dataset of labeled products in Swiss supermarket shelves. This dataset is a task-adaption one-shot learning dataset. For every product there is one labeled studio quality image of the product (in vitro) in the training dataset and the validation dataset consists of labeled frames in 29 videos with labeled bounding boxes in shelves in a shop realistic environment (in situ). low-quality images.

Website

Grozi-3.2K

Please cite the paper as follows:
Marian George and Christian Floerkemeier. "Recognizing Products: A Per-exemplar Multi-label Image Classification Approach." European Conference on Computer Vision. Springer, Cham, 2014

Version of GroZi120 with more products and higher quality images. A dataset of labeled products in Swiss supermarket shelves. This dataset is a task-adaption one-shot learning dataset. For every product there is one labeled studio quality image of the product (in vitro) in the training dataset and the validation dataset consists of images taken from a camcorder video with labeled bounding boxes on in shelves in a shop realistic environment (in situ).

Download (will forward to Google Pages Website)

SKU110K

Please cite the paper as follows:
Eran Goldman, Roei Herzig, Aviv Eisenschtat, Jacob Goldberger, Tal Hassner. "Precise Detection in Densely Packed Scenes." Computer Vision and Pattern Recognition. 2019

A dataset of labeled bounding boxes on realistic images of products shelves in supermarkets.

Github

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