CSRNet-Simple-Pytorch

This is an simple and clean implemention of CVPR 2018 paper "CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes".

Requirement

1. Install pytorch 1.0.0+
2. Install tensorboardX
3. Clone this repository

   git clone https://github.com/CommissarMa/CSRNet-pytorch.git
   

   We'll call the directory that you cloned CSRNet-pytorch as ROOT.

Data Setup

1. Download ShanghaiTech Dataset from Dropbox or Baidu Disk (code: a2v8)
2. Put ShanghaiTech Dataset in 'ROOT/data'. You can find two python scripts in 'data_preparation' folder which are used to generate ground truth density-map for ShanghaiTech PartA and PartB respectively. (Mind that you need move the script to corresponding sub dataset folder like 'ROOT/data/part_A_final' and run it)

Train

1. Modify the dataset root in 'config.py'
2. Run 'train.py'
3. Open the command line and type in 'tensorboard --logdir=ROOT/runs', then browse 'localhost:6006' to see the visual result.

Testing

1. Run 'test.py' for calculate MAE of test images or just show an estimated density-map.

Other notes

1. We trained the model and got 67.74 MAE at 124-th epoch on ShanghaiTech PartA. Got 8.68 MAE on PartB at 94-th epoch.
2. If you are new to crowd counting, we recommand you to take a look at Crowd_counting_from_scratch first. It is a tutorial for crowd counting beginner.