Computational Intelligence and Neuroscience / 2020 / Article / Tab 5

Review Article

Deep Learning for Retail Product Recognition: Challenges and Techniques

Table 5

Detailed information of several public datasets.

ScenarioDataset#product categoriesTraining setTest set
#instances per image#number of images#instances per image#number of images

On-shelfGroZi-120 dataset (http://grozi.calit2.net/grozi.html)120Multiple676Multiple4,973
GroZi-3.2k dataset (https://sites.google.com/view/mariangeorge/datasets)27/80Single8,350Multiple3,235
Freiburg Grocery dataset (https://github.com/PhilJd/freiburg_groceries_dataset)25Multiple (one class)4,947Multiple74
Cigarette dataset (https://github.com/gulvarol/grocerydataset)10Single3,600Multiple354
Grocery Store dataset (https://github.com/marcusklasson/GroceryStoreDataset)81Multiple (one class)2,640Multiple (one class)2,458

CheckoutD2S dataset (https://www.mvtec.com/company/research/datasets/mvtec-d2s/)60Single4,380Multiple16,620
RPC dataset (https://rpc-dataset.github.io/)200/17Single53,739Multiple30,000

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