# Locount Dataset

[Rethinking Object Detection in Retail Stores](http://arxiv.org/abs/2003.08230).

## Introduction

The convention standard for object detection uses a bounding box to represent each individual object instance. However, it is not practical in the industry-relevant applications in the context of warehouses due to severe occlusions among groups of instances of the same categories. In this paper, we propose a new task, ie, simultaneously object localization and counting, abbreviated as *Locount*, which requires algorithms to localize groups of objects of interest with the number of instances. However, there does not exist a dataset or benchmark designed for such a task. To this end, we collect a large-scale object localization and counting dataset with rich annotations in retail stores, which consists of 50,394 images with more than 1.9 million object instances in 140 categories.

```
@inproceedings{Cai2020Locount,
    title={Rethinking Object Detection in Retail Stores},
    author={Yuanqiang Cai and Longyin Wen and Libo Zhang and Dawei Du and Weiqiang Wang and Pengfei Zhu},
    booktitle={arXiv preprint arXiv:2003.08230},
    year={2020}
}
```

## Results and Models