Review Article
Deep Learning for Retail Product Recognition: Challenges and Techniques
Table 5
Detailed information of several public datasets.
| | Scenario | Dataset | #product categories | Training set | Test set | | #instances per image | #number of images | #instances per image | #number of images |
| | On-shelf | GroZi-120 dataset (http://grozi.calit2.net/grozi.html) | 120 | Multiple | 676 | Multiple | 4,973 | | GroZi-3.2k dataset (https://sites.google.com/view/mariangeorge/datasets) | 27/80 | Single | 8,350 | Multiple | 3,235 | | Freiburg Grocery dataset (https://github.com/PhilJd/freiburg_groceries_dataset) | 25 | Multiple (one class) | 4,947 | Multiple | 74 | | Cigarette dataset (https://github.com/gulvarol/grocerydataset) | 10 | Single | 3,600 | Multiple | 354 | | Grocery Store dataset (https://github.com/marcusklasson/GroceryStoreDataset) | 81 | Multiple (one class) | 2,640 | Multiple (one class) | 2,458 |
| | Checkout | D2S dataset (https://www.mvtec.com/company/research/datasets/mvtec-d2s/) | 60 | Single | 4,380 | Multiple | 16,620 | | RPC dataset (https://rpc-dataset.github.io/) | 200/17 | Single | 53,739 | Multiple | 30,000 |
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