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PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
| Date | Stars |
|---|---|
| 2026-07-24 | 273 |
| 2026-07-25 | 272 |
| 2026-07-28 | 272 |
| 2026-07-30 | 272 |
| 2026-08-06 | 272 |
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# PCC Net: Perspective Crowd Counting via Spatial Convolutional Network This is an official implementation of the paper "PCC net" (PCC Net: Perspective Crowd Counting via Spatial Convolutional Network).  In the paper, the experiments are conducted on the three populuar datasets: Shanghai Tech, UCF_CC_50 and WorldExpo'10. To be specific, Shanghai Tech Part B contains crowd images with the same resolution. For easier data prepareation, we only release the pre-trained model on ShanghaiTech Part B dataset in this repo. ## Bracnhes 1. [ori_pt0.2_py2](https://github.com/gjy3035/PCC-Net/tree/ori_pt0.2_py2): the original version. 2. [ori_pt1_py3](https://github.com/gjy3035/PCC-Net): the current version. 3. [vgg_pt1_py3](https://github.com/gjy3035/PCC-Net/tree/vgg_pt1_py3): vgg-backbone PCC Net (higher performance). ## Requirements - Python 3.x - Pytorch 1.x - TensorboardX (pip) - torchvision (pip) - easydict (pip) - pandas (pip) ## Data preparation 1. Download the original ShanghaiTech Dataset [Link: [Dropbox ](https://www.dropbox.com/s/fipgjqxl7uj8hd5/ShanghaiTech.zip?dl=0)/ [BaiduNetdisk](https://pan.baidu.com/s/1nuAYslz)] 2. Resize the images and the locations of key points. 3. Generate the density maps by using the [code](https://github.com/aachenhang/crowdcount-mcnn/tree/master/data_preparation). 4. Generate the segmentation maps. We also provide the processed Part B dataset for training. [[Link](https://mailnwpueducn-my.sharepoint.com/:u:/g/personal/gjy3035_mail_nwpu_edu_cn/EcMLqr9XuH1ChAgkqpxL_6kBK9EyCmIuXMxTb09FrjMYow?e=LJnOcC)] ## Training model 1. Run the train_lr.py: ```python train_lr.py```. 2. See the training outputs: ```Tensorboard --logdir=exp --port=6006```. In the experiments, training and tesing 800 epoches take 21 hours on GTX 1080Ti. ## Expermental results ### Quantitative results We show the Tensorboard visualization results as below:  The mae and mse are the results on test set. Others are triaining loss. ### Visualization results Visualization results on the test set as below:  Column 1: input image; Column 2: density map GT; Column 3: density map prediction; Column 4: segmentation map GT; Column 5: segmentation map prediction. ## Citation If you use the code, please cite the following paper:
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