When time allows, I will upload code that is related to the paper above, such that the results in the paper can be reproduced. The classes corresponding label (an integer) is also included in addition to the paths to their iconic image and the product description.įeel free to download the dataset and apply it to your model. The 81 fine-grained classes and their coarse-grained classes can be found in classes.csv in the folder dataset. Each row in these two files consists of the path to an image and its fine-grained label followed by its coarse-grained label, where both labels are represented as integers. The files train.txt, val.txt and test.txt in the folder dataset includes the paths to the images in the training, validation and test set respectively.
The dataset was presented in the paper "A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels", which appeared at WACV 2019.
For each fine-grained class, we have downloaded an iconic image and a product description of the item, where some samples of these can be seen on this page below. the fine-grained classes 'Royal Gala' and 'Granny Smith' belong to the same coarse-grained class 'Apple'. The 81 classes are divided into 42 coarse-grained classes, where e.g. We ended up with 5125 natural images from 81 different classes of fruits, vegetables, and carton items (e.g. All natural images was taken with a smartphone camera in different grocery stores. This repository contains the dataset of natural images of grocery items.
0 kommentar(er)
