deepcount

February 15, 2020 · View on GitHub

Deep Density-aware Count Regressor

a state-of-the-art method for crowd counting
fast, easy to implement and scale
paper: https://arxiv.org/abs/1908.03314

PaddlePaddle Implementation Branch

https://github.com/GeorgeChenZJ/deepcount/tree/paddle

Installation

Install requirements: Python 2.7, Tensorflow >= 1.8, PyTorch >= 0.4, Pillow, tqdm, scipy
Download pretrained vgg model from https://download.pytorch.org/models/vgg16-397923af.pth
Place vgg16-397923af.pth under the working directory

Data

Prepare the dataset first and modify data.py to make the data accessible. See data.py for detail. The current code of data.py is for Shanghai Tech Part B. Before training the model download Shanghai Tech dataset should be downloaded (https://drive.google.com/file/d/16dhJn7k4FWVwByRsQAEpl9lwjuV03jVI/view). Change 'data_dir' in data.py and relevant codes for loading the dataset.

Run

Set batch size and number of devices in train.py and then run

python train.py

Note that the code is written to be plain but not optimally efficient.

Trained Weights

We provide trained model on ShanghaiTech Part B: https://drive.google.com/open?id=1qaYXLX5vYS0prhDFdYLWSr4YFDf_geQz