视频讲解1：Bilibili视频讲解
代码下载：https://github.com/KeepTryingTo/CrowdCounting-framework-PyTorch
本项目主要是实现了一个人群计数框架，学习者可以直接一键使用该框架生成密度图数据集，并且该框架也给出了一些常见的损失函数实现，只需要直接加入即可使用。该框架最大的亮点是，学习者不需要去改变任何代码即可训练模型，如果学习者想要加入自己的模型，直接实现即可嵌入到该训练框架中。该框架帮助学习者不需要去关注除了核心部分之外的其他代码，帮助学习者更专心的相关算法。 并且还实现了采用分布式框架训练模型。其中有关该项目参考的其他开源代码已经在项目末尾给出了链接。
支持的功能
√\] 单GPU，CPU或者多GPU分布式训练 \[√\] 自定义标注框数据集生成密度图 \[√\] 一键生成常见数据集的密度图之后即可进行训练，比如ShangHai_partA, ShangHai_partB,NWPU,QNRF,UCF_CC_50,JHUC-CROWD++ \[√\] 提供了常用的默认backbone网络模型 \[√\] 提供了常用的损失函数 \[√\] 日志记录以及模型保存（保存最好结果模型） \[√\] 模型参数量以及flops统计 \[√\] 绘制结果MAE训练曲线 \[√\] 可视化真实的密度图npy以及可视化模型预测密度图 \[√\] 提供了常用的学习率调度器 \[√\] 统计GPU的使用情况 更多使用说明请看： [https://github.com/KeepTryingTo/CrowdCounting-framework-PyTorch/blob/main/zh_README.md](https://github.com/KeepTryingTo/CrowdCounting-framework-PyTorch/blob/main/zh_README.md "https://github.com/KeepTryingTo/CrowdCounting-framework-PyTorch/blob/main/zh_README.md") 参考链接 [人群计数中常用数据集的总结以及使用方式（Python/PyTorch）](https://mydreamambitious.blog.csdn.net/article/details/147144537?spm=1011.2415.3001.5331 "人群计数中常用数据集的总结以及使用方式（Python/PyTorch）") [论文CrowdCLIP（基于CLIP的无监督人群计数模型）详解（PyTorch,Pytorch_Lighting）](https://mydreamambitious.blog.csdn.net/article/details/147851355?spm=1011.2415.3001.5331 "论文CrowdCLIP（基于CLIP的无监督人群计数模型）详解（PyTorch,Pytorch_Lighting）") [论文The Effectiveness of a Simplified Model Structure for Crowd Counting（FFNet）详解](https://mydreamambitious.blog.csdn.net/article/details/141355068?spm=1011.2415.3001.5331 "论文The Effectiveness of a Simplified Model Structure for Crowd Counting（FFNet）详解") [论文Domain-General Crowd Counting in Unseen Scenarios（DCCUS）详解以及对应代码详解](https://mydreamambitious.blog.csdn.net/article/details/142730047?spm=1011.2415.3001.5331 "论文Domain-General Crowd Counting in Unseen Scenarios（DCCUS）详解以及对应代码详解") [论文Distribution Matching for Crowd Counting详解](https://mydreamambitious.blog.csdn.net/article/details/143133438?spm=1011.2415.3001.5331 "论文Distribution Matching for Crowd Counting详解") [论文Distribution Matching for Crowd Counting中人群统计损失（C Loss），最优化传输损失（OT Loss）以及总的变化损失（TV Loss）](https://mydreamambitious.blog.csdn.net/article/details/143219789?spm=1011.2415.3001.5331 "论文Distribution Matching for Crowd Counting中人群统计损失（C Loss），最优化传输损失（OT Loss）以及总的变化损失（TV Loss）") [论文CLIP-EBC（基于CLIP的人群统计模型）详解](https://mydreamambitious.blog.csdn.net/article/details/147819012?spm=1011.2415.3001.5331 "论文CLIP-EBC（基于CLIP的人群统计模型）详解") [论文CLIP-Count（基于文本指导的零样本目标计数）详解（PyTorch）](https://mydreamambitious.blog.csdn.net/article/details/147873916?spm=1011.2415.3001.5331 "论文CLIP-Count（基于文本指导的零样本目标计数）详解（PyTorch）") https://github.com/cvlab-stonybrook/DM-Count https://github.com/gjy3035/Awesome-Crowd-Counting https://github.com/gjy3035/C-3-Framework https://github.com/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios https://github.com/openai/CLIP https://mydreamambitious.blog.csdn.net/article/details/142730047?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/147144537?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/143133438?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/143219789?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/144692297?spm=1011.2415.3001.5331 https://github.com/erdongsanshi/FFNet https://mydreamambitious.blog.csdn.net/article/details/141355068?spm=1011.2415.3001.5331 https://github.com/KeepTryingTo/PyTorch-DeepLearning-Visual-LLM https://github.com/KeepTryingTo https://blog.csdn.net/Keep_Trying_Go/article/details/154913403 https://mydreamambitious.blog.csdn.net/article/details/154789450?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/154443638?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/148124251?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/148300284?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/148227727?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/147873916?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/147851355?spm=1011.2415.3001.5331 https://mydreamambitious.blog.csdn.net/article/details/147819012?spm=1011.2415.3001.5331