In this project, we modify 3 representative state-of-art detectors and train them on CrowdHuman dataset. Faster R-CNN with FPN, RepPoints and Object as Points are investigated. They represent three typical new ideas in general object detection problem respectively: classical anchor-based two-stage detector with feature pyramid, deformable-based anchor free two-stage detector and anchor free one-stage detection. Our Faster R-CNN with FPN and RepPoints are implemented based on MMdetection (as shown in reference).

After install Anaconda:

1. [Optional but recommended] create a new conda environment.

   conda create --name CenterNet python=3.6
   

   And activate the environment.

   conda activate CenterNet
   

2. Install pytorch0.4.1:

   conda install pytorch torchvision -c pytorch
   

3. Install COCOAPI:

   # COCOAPI=/path/to/clone/cocoapi
   git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
   cd $COCOAPI/PythonAPI
   make
   python setup.py install --user
   

4. Clone this repo:

   CenterNet_ROOT=/path/to/clone/CenterNet
   git clone https://github.com/xingyizhou/CenterNet $CenterNet_ROOT
   

5. Install the requirements

   pip install -r requirements.txt
   

6. Compile and install deformable convolutional (from DCNv2).

python setup.py develop
 

• Use the ./src/tools/crowd/get_crowd.sh to download the crowd dataset and

• Place the data (or create symlinks) to make the data folder like:

  ${CenterNet_ROOT}
  |-- data
  `-- |-- crowd
      `-- |-- annotations
          |   |-- crowd_val.json
          |   |-- crowd_train.json
          `-- images
              |-- 273271,1a0d6000b9e1f5b7.jpg
              |-- ...
  

Analysis and download the dataset, use the file under the src\tools\crowd, where you can find our codes for visualization anlysis, upper bound analysis, data statistics analysis, image saliency analysis.....

Use the ./experiments/train_crowd.shfor training and testing and saliency analysis.

training setting example:

CUDA_VISIBLE_DEVICES=1,2 python src/mm_train.py ./experiments/faster_rcnn_r50_fpn.py

testing

python src/mm_test.py ./experiments/faster_rcnn_r50_fpn.py work_dirs/reppoints_moment_r50_fpn_2x/latest.pth --json_out ./results/reppoints_1333_800.json

Center Net: https://github.com/xingyizhou/CenterNet

MMdetection: https://github.com/open-mmlab/mmdetection