A crowd-counting network with spatial and channel attention modules reports lower mean absolute error than several prior models on four public datasets.
C^3 Framework: An Open-source PyTorch Code for Crowd Counting
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abstract
This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F). The contributions of C$^3$F are in three folds: 1) Some solid baseline networks are presented, which have achieved the state-of-the-arts. 2) Some flexible parameter setting strategies are provided to further promote the performance. 3) A powerful log system is developed to record the experiment process, which can enhance the reproducibility of each experiment. Our code is made publicly available at \url{https://github.com/gjy3035/C-3-Framework}. Furthermore, we also post a Chinese blog\footnote{\url{https://zhuanlan.zhihu.com/p/65650998}} to describe the details and insights of crowd counting.
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2019 1verdicts
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SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
A crowd-counting network with spatial and channel attention modules reports lower mean absolute error than several prior models on four public datasets.