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CCTrans: Simplifying and Improving Crowd Counting with Transformer(Code reproduction)

  • Code reproduction
  • Original paper Link

Overview

  • Presentate only the experiment on dataset ShanghaiTech Part A (loss: DM-Count)
  • ShanghaiTech Part A
Code MAE MSE
PAPER 54.8 86.6
This code 54.20 88.97

Our code reaches this result with the standard hyperparameter set in code. Trained with batch-size=8 for around 1500 epoch(as said in the paper). Best validation at around epoch 606

code framework

  • adopt code of DM-Count.
  • link

Training

Take a look at the arguments accepted by train.py

  • update root "data-dir" in ./train.py.
  • load pretrained weights of ImageNet-1k in ./Networks/ALTGVT.py.
  • pretrained weights link
  • [new] Added wandb integration. If you want to log with wandb, set --wandb 1 in train.py after having logged in to wandb (wandb login in console)
  • launch with python train.py

Testing

  • python test_image_patch.py
  • Due to crop training with size of 256x256, the validation image is divided into several patches with size of 256x256, and the overlapping area is averaged.
  • Download the pretrained model from Baidu-Disk(提取码: se59) link

Visualization

  • python vis_densityMap.py
  • save to ./vis/part_A_final

Environment

See requirements.txt

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