Dense-Scale-Network-for-Crowd-Counting
An unofficial implement of paper "Dense Scale Network for Crowd Counting".
Dataset setup
Download the shanghaitech dataset from here, UCF-QNRF dataset from here.
Data preparation
In make_sh_gt.py, modify variable root in line 18 to your dataset path and set the min_size in line 16 for image. Then run the .py file. It will save images and .h5 file in root/{dataset}_preprocessed/train/ and root/{dataset}_preprocessed/test/.
Train
In main.py, set train_path to root/{dataset}_preprocessed/train/ and test_path to root/{dataset}_preprocessed/test/ in line 81 and 82. Also specify the save_path.
When training shanghaitech PartA dataset, the model shows faster convergence if learning rate is set as 1e-4 compared to 1e-5 which is claimed by the paper.
Test
Test on one image
python test_one_image.py --gpu 0 --model_path pretrained_model_path --test_img_path your_image_path
Test on a dataset
python test_dataset.py --gpu 0 --model_path pretrained_model_path --test_img_dir your_image_directory
Result
| Dataset | MAE | MSE |
|---|---|---|
| sha | 69.35 | 104.4 |
| shb | 8.58 | 14.87 |
| qrnf | tbd | tbd |
Anyone interested in implementing crowd counting models is welcomed to contact me.