Update README.md

In order to solve the practical industrial application problems and promote the academic research of the problem, we put forward the necessary elements of the task: 

*problem definition*, *Locount dataset*, *evaluation protocol* and *baseline method*. This work was accepted by the International Artificial Intelligence Conference AAAI 2021.

!["Figure 1: The previous object recognition datasets in grocery stores have focused on image classification, i.e., (a) Supermarket

Produce (Rocha et al. 2010) and (b) Grozi-3.2k (George and Floerkemeier 2014), and object detection, i.e., (c) D2S (Follmann

et al. 2018), (d) Freiburg Groceries (Jund et al. 2016), and (e) Sku110k (Goldman et al. 2019). We introduce the Loccount task,

aiming to localize groups of objects of interest with the numbers of instances, which is natural in grocery store scenarios, shown

in the last row, i.e., (f), (g), (h), (i), and (j). The numbers on the right hand indicate the numbers of object instances enclosed in

the bounding boxes. Different colors denotes different object categories. Best viewed in color and zoom in."](Images/dataset-comparison.jpg)

<div align=center><img src="Images/dataset-comparison.jpg" width="1100" height="400" /></div>

Figure 1: The previous object recognition datasets in grocery stores have focused on image classification, i.e., (a) Supermarket

Electrical Appliances (e.g., *Microwave Oven* and *Socket*), Storage Appliances (e.g., *Trash* and *Stool*), Kitchen Utensils (e.g., *Forks* and *Food Box*), and Stationery and Sporting Goods (e.g., *Skate* and *Notebook*). 

There are various factors challenging the performance of algorithms, such as scale changes, illumination variations, occlusion, similar appearance, clutter background, blurring and deformation, *etc*.

![Figure 2: Category hierarchy of the large-scale localization and counting dataset in

the shelf scenarios.](Images/category-tree.png)

<div align=center><img src="Images/category-tree.png" width="1100" height="400" /></div>

Figure 2: Category hierarchy of the large-scale localization and counting dataset in

the shelf scenarios.

## Evaluation protocol