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Thus, the counting task is formulated as the multi-class classification task, which use a FC layer to determine the bin index for instance number.

Thus, the counting task is formulated as the multi-class classification task, which use a FC layer to determine the bin index for instance number.

We conduct several experiments of the state-of-the-art object detectors and the proposed CLCNet method on the proposed dataset, to demonstrate the effectiveness of CLCNet, in Table 2.

We conduct several experiments of the state-of-the-art object detectors and the proposed CLCNet method on the proposed dataset, to demonstrate the effectiveness of CLCNet, Table 2 and Fig. 4.

![Table 2: Comparison results of the algorithms on the proposed dataset. Detection results of all comparison methods on the

![Table 2: Comparison results of the algorithms on the proposed dataset. Detection results of all comparison methods on the

proposed dataset. The mark lc on the upper right corner indicates that its value is computed by the proposed metrics](https://isrc.iscas.ac.cn/gitlab/research/locount-dataset/-/blob/master/Images/Experiment-results.png)

proposed dataset. The mark lc on the upper right corner indicates that its value is computed by the proposed metrics](https://isrc.iscas.ac.cn/gitlab/research/locount-dataset/-/blob/master/Images/Experiment-results.png)

![Figure 4: Qualitative results of the proposed CLCNet method on the Locount dataset.](https://isrc.iscas.ac.cn/gitlab/research/locount-dataset/-/blob/master/Images/show-results.jpg)

## Citation

## Citation

If you find this dataset useful for your research, please cite

If you find this dataset useful for your research, please cite

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