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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.](https://isrc.iscas.ac.cn/gitlab/research/locount-dataset/-/blob/master/Images/category-tree.png)

## Evaluation protocol

To fairly compare algorithms on the *Locount* task, we design a new evaluation protocol, which penalizes algorithms for missing object instances, 

instances enclosed in the bounding box, where N is the total number of stages. For more detailed definitions, please refer to the [paper](http://arxiv.org/abs/2003.08230).

The counting accuracy threshold for the positive/negative sample generation is determined by the architecture design of CLCNet, which is described as follows.

![Figure 3: The architecture of our CLCNet for the Locount task. The cubes indicate the output feature maps from the convolutional layers or RoIAlign operation.

The numbers in the brackets indicate the range of counting number in each stage.](https://isrc.iscas.ac.cn/gitlab/research/locount-dataset/-/blob/master/Images/framework.png)

We use the same architecture and configuration as Cascade R-CNN for the box-regression and box-classification layers. For the instance counting layer, 

a direct strategy is to use a FC layer to regress a floating point number, indicating the number of instances, called *count-regression strategy*. 

However, the numbers of instances enclosed in the bounding boxes are integers, leading challenges for the network to regress accurately.