C^3 Framework: An Open-source PyTorch Code for Crowd Counting

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.

Technical report

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

Cited by in corpus (17)

• NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
• SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
• CNN-based Density Estimation and Crowd Counting: A Survey
• Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting
• Learning Independent Instance Maps for Crowd Localization
• Dense Crowds Detection and Counting with a Lightweight Architecture
• Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance
• LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds
• AutoScale: Learning to Scale for Crowd Counting and Localization
• Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction
• Focus on Semantic Consistency for Cross-domain Crowd Understanding
• Multi-Level Attentive Convoluntional Neural Network for Crowd Counting
• Crowd Density Forecasting by Modeling Patch-based Dynamics
• Enhanced Information Fusion Network for Crowd Counting
• DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks
• Pixel-wise Crowd Understanding via Synthetic Data
• Learning to Rank for Active Learning: A Listwise Approach