pytorch版本实现的CSRNet，使用的数据集为shanghai_tech数据集，数据集下载参考我的其他上传资源，太大了，一下上传不上去。

2018年经典的人群计数领域论文csr net的ppt,自己组会的时候参照论文大致做的。ppt里面还是包含了大致的框架以及文章部分内容，可以当成是文献分享的一个思路参考吧。

人群密度检测的shanghai_tech数据集（其中part_A的训练集已经生成h5文件），另外使用pytorch实现的CSRNet人群密度检测可以我的其他上传。（太大了，上传不上去）

使用pytorch实现了CSRNet人群计数模型的复现，如果下载文档之后有任何问题均可以私信博主进行讨论

本资源为在caffe框架下训练好的人群计数模型csrnet，训练数据集为shanghai-partB,测试结果MAE:11.7783;MSE:18.5910，较论文差点，但Sacnn模型效果提升很多

We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps. The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations. CSRNet is an easy-trained model because of its pure convolutional structure. We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance. In the ShanghaiTech Part_B dataset, CSRNet achieves 47.3% lower Mean Absolute Error (MAE) than the previous state-of-the-art method. We extend the targeted applications for counting other objects, such as the vehicle in TRANCOS dataset. Results show that CSRNet significantly improves the output quality with 15.4% lower MAE than the previous state-of-the-art approach.