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.
We also provide the processed Part B dataset for training. [Link]
python train_lr.py
.Tensorboard --logdir=exp --port=6006
.In the experiments, training and tesing 800 epoches take 21 hours on GTX 1080Ti.
We show the Tensorboard visualization results as below: The mae and mse are the results on test set. Others are triaining loss.
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.
If you use the code, please cite the following paper: