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README.md

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 89.76 142.23
shb to be done tbd
qrnf tbd tbd

Anyone interested in implementing crowd counting models is welcomed to contact me.

About

An unofficial implement of paper "Dense Scale Network for Crowd Counting", link: https://arxiv.org/abs/1906.09707

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