# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 4 Related Datasets
### Datasets for Grocery Product Detection and Recognition  
Several image datasets have been made available, targeting different aspects or scenarios related to grocery product detection or recognition. Figure 4 presents some examples of possible images. Next, these datasets are briefly described and a comparison is summarised in Table 2.  
**Products-6K**[8] provides images from Greek supermarkets. The dataset contains 12,917 studio-quality images associated with 6348 stock-keeping units (SKUs). The reference images were captured in a clean environment with white background, while 373 query images of 104 classes were captured under real supermarket conditions, taken by a mobile phone camera. Products can be displayed on the shelf or in hand, with photos taken from different sides of the product. A fine-grained description and some textual information are provided, but texts were not labelled by human annotators and text location information is missing. The annotations, whether class or textual, are in Greek as well as the description found on the packages themselves. **GroZi-120**[47] consists of 120 Swiss market product categories that are very different from each other. The training set includes 676 images selected from the web containing products captured under ideal conditions. Training images contain just one single product instance viewed from different front-side perspectives. The test set has 4973 frames of 29 real videos from grocery racks to represent the challenging natural environment. The product images that appeared in the frames were cropped to create a query image set. As these images are of products on the shelf, only the front view was captured and the low resolution of the images adds another degree of difficulty. Ground-truth includes the image class and bounding box. **GroZi-3.2k**[45] contains image of Swiss products on supermarket racks. Inside the food category, there are 27 coarse classes and 3235 training images. Training images collected from the web are taken in a controlled environment with a white background. Query set images are composed of 680 high-quality images captured from 5 real-life retail stores using a mobile phone. Ground-truth includes relative coordinates of the bounding boxes and product labels, such as coffee, cereals and milk. **Grocery Store**[46] includes images of fruits, vegetables and carton items captured with a smartphone camera in different Swedish grocery stores. The dataset also includes a studio-quality image per item. For food label reading purposes, 1745 images of carton items (e.g. juice, milk, yoghurt) can be used. Most of the products are held by a hand and a few of them are displayed on shelves. A total of 31 fine-grained classes are annotated, which includes the super category and the brand with the flavour/type. Other files are accessible, one with a brief description in English and the other with more detailed information in Swedish, such as ingredients, nutrition values, manufacturer, volume and manufacturing country. It should be noted that the dataset is designed for the classification and recognition of objects and not for their detection. **Freiburg Groceries Dataset**[48] consists of 4947 images of 25 coarse grocery classes. The training images were taken predominantly in stores, but some in apartments and offices in Germany, using four different camera phones. In addition to having photos from several domains, the products are seen from various perspectives and light conditions. To have a variable degree of clutter, images contain one or several instances of one of 25 classes. As annotated classes, we have cereal, pasta and cake, among others. Data are labelled only at the image level. Figure 4: Image examples of a set of publicly available datasets: (a) CAPG dataset [44]; (b) GroZi 3.2k dataset [45]; (c) GroceryStore dataset [46]. **D2S dataset**[49] is a dataset designed for automatic checkout machines or inventory systems.