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RP2K

RP2K

5.9G
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Smart Retailing Classification

Pipeline of our data collection process. Our photo collectors were first distributed in over 500 different retail stores......

数据结构 ? 5.9G

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    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


    Categorized information of the RP2K dataset

    16480905



    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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