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Improving Crowd Counting by Boosting Scale Invariance

Improving Crowd Counting by Boosting Scale Invariance

crowdcount-stackpool

Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance.

Pytorch implementation of paper "Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance".

Dependency

  1. Python 2.7
  2. PyTorch 0.4.0

Data Setup

  1. Download ShanghaiTech Dataset from
    Dropbox: https://www.dropbox.com/s/fipgjqxl7uj8hd5/ShanghaiTech.zip?dl=0
    Baidu Disk: http://pan.baidu.com/s/1nuAYslz
  2. Create Directory mkdir ./data/original/shanghaitech/
  3. Save "part_A_final" under ./data/original/shanghaitech/
    Save "part_B_final" under ./data/original/shanghaitech/
  4. cd ./data_preparation/
    Run create_gt_test_set_shtech.m in matlab to create ground truth files for test data
    Run create_training_set_shtech.m in matlab to create training and validataion set along with ground truth files

Train

  1. To train Deep Net+vanilla pooling on ShanghaiTechA, edit configurations in train.py

    pool = pools[0] 
    

    To train Deep Net+stacked pooling on ShanghaiTechA, edit configurations in train.py

    pool = pools[1] 
    
  2. Run python train.py respectively to start training

Test

  1. Follow step 1 of Train to edit corresponding pool in test.py
  2. Edit model_path in test.py using the best checkpoint on validation set (output by training process)
  3. Run python test.py respectively to compare them!

Note

  1. To try pooling methods (vanilla pooling, stacked pooling, and multi-kernel pooling) described in our paper:

    Edit pool in train.py and test.py

  2. To evaluate on datasets (ShanghaiTechA, ShanghaiTechB) or backbone models (Base Net, Wide-Net, Deep-Net) described in our paper:

    Edit dataset_name or model in train.py and test.py

GitHub