RP2K
Scene：
Smart RetailingData Type：
Classification
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Data Preview ?
5.9G
Data Structure ?
*数据结构实际以真实数据为准
Pipeline of our data collection process. Our photo collectors were first distributed in over 500 different retail stores and collected over 10k high-resolution shelf images. Then we use a pre-trained detection model to extract the bounding boxes of potential objects of interests. After that, our human annotators discard the incorrect bounding boxes, including heavily occluded images and images that is not a valid retail product. The remaining images are annotated by the annotators.
Categorized information of the RP2K dataset
Comparing to other datasets, our dataset shows considerably larger number of categories, while maintaining a decent amount of images.
Long tail problem in fine-grained recognition. With the decreased number of available images, the recognition accuracy is tend to decrease.
ATTENTION
This
dataset and code packages are free for academic usage. You can run them
at your own risk. For other purposes, please contact the corresponding
author Jingtian Peng (pjt@pinlandata.com)
@article{peng2020rp2k,
title={RP2K: A Large-Scale Retail Product Dataset forFine-Grained Image Classification},
author={Peng, Jingtian and Xiao, Chang and Wei, Xun and Li, Yifan},
journal={arXiv preprint arXiv:2006.12634},
year={2020}
}
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