Hybrid attention network based on progressive embedding scale-context for

crowd counting

• The paper is under review.

• Paper Link

Overview

Data processing

• Shanghai Tech A randomly crop 4 patch of 128x128 from each image.
• Shanghai Tech B randomly crop 4 patch of 256x256 from each image.
• UCF-CC-50 randomly crop 8 patch of 128x128 from each image.
• QNRF randomly crop 8 patch of 128x128 from each image.
• NWPU-Crowd randomly crop 1 patch of 576x768 from each image.

Training

• update root "HOME and DATASET" in ./config.py.
• python train.py

Testing

• python eval.py

Evaluation metrics

• MSE and MSE

Visualization

• python vis.py

Environment

python >=3.6 
pytorch >=1.5
opencv-python >=4.0
scipy >=1.4.0
h5py >=2.10
pillow >=7.0.0
imageio >=1.18