# RPC: A Large-Scale Retail Product Checkout Dataset Paper : [RPC: A Large-Scale Retail Product Checkout Dataset](https://arxiv.org/abs/1901.07249) Authors: **[Xiu-Shen Wei](http://lamda.nju.edu.cn/weixs)     [Quan Cui](mailto:cui-quan@toki.waseda.jp)    [Lei Yang](https://github.com/DIYer22)    Peng Wang     Lingqiao Liu** Project Page : [RPC Dataset Project Page](http://rpc-dataset.github.io) Introduction Kernel: [Introduce the RPC-Dataset](https://www.kaggle.com/diyer22/introduce-rpc-dataset) ## 1. Abstract Over recent years, emerging interest has occurred in integrating computer vision technology into the retail industry. Automatic checkout (ACO) is one of the critical problems in this area which aims to automatically generate the shopping list from the images of the products to purchase. The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products. Despite its significant practical and research value, this problem is not extensively studied in the computer vision community, largely due to the lack of a high-quality dataset. To fill this gap, in this work we propose a new dataset to facilitate relevant research. Our dataset enjoys the following characteristics: (1) It is by far the largest dataset in terms of both product image quantity and product categories. (2) It includes single-product images taken in a controlled environment and multi-product images taken by the checkout system. (3) It provides different levels of annotations for the checkout images. Comparing with the existing datasets, ours is closer to the realistic setting and can derive a variety of research problems. Besides the dataset, we also benchmark the performance on this dataset with various approaches. ## 2. Dataset information More details of the dataset can be found via [RPC Dataset Project Page#Dataset](https://rpc-dataset.github.io/#3-our-rpc-dataset). 2.1 Collection equipment for single product images (training set)  2.2 Different clutter levels for checkout images (val/test sets)  **Notice**: We also provide two empty white board pictures as background [here](https://github.com/RPC-Dataset/RPC-Dataset.github.io/tree/master/imgs/backgrounds) ## 3. Data format RPC-dataset has the same data structure as the data struecture of [the COCO dataset](http://cocodataset.org/#format-data)'s object detection annotation format. More details of the data format can be found via [Kernel: Introduce the RPC-Dataset](https://www.kaggle.com/diyer22/introduce-rpc-dataset). ## 4. Leaderboard [**RPC-Leaderboard**](https://github.com/RPC-Dataset/RPC-Leaderboard) If you have been successful in creating a model based on the training set and it performs well on the validation set, we encourage you to run your model on the test set. The [`rpctool`](https://github.com/DIYer22/retail_product_checkout_tools) (in the next section in this project page) will contribute to return the corresponding results of the evaluation metrics. You can submit your results on the RPC leaderboard by creating a new issue. Your results will be ranked in the leaderboard and to benchmark your approach against that of other machine learners. We are looking forward to your submission. Please click [here](https://github.com/RPC-Dataset/RPC-Leaderboard/issues) to submit. ## 5. RPC-tool [`rpctool`](https://github.com/DIYer22/retail_product_checkout_tools): A Python package for evaluating your methods on the RPC dataset. It can return several evaluation metrics. More information can be found in [`rpctool`](https://github.com/DIYer22/retail_product_checkout_tools). ## 6. Others If downloading from Kaggle is not accessable, you can alternatively download the dataset using [Baidu Drive](https://pan.baidu.com/s/1vrrLaSpJe5JxT3zhYfOaog).