CenterNet

Dataset preparation

If you want to reproduce the results in the paper for benchmark evaluation and training, you will need to setup dataset.

COCO

• Download the images (2017 Train, 2017 Val, 2017 Test) from coco website.
• Download annotation files (2017 train/val and test image info) from coco website.

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

  ${CenterNet_ROOT}
  |-- data
  `-- |-- coco
      `-- |-- annotations
          |   |-- instances_train2017.json
          |   |-- instances_val2017.json
          |   |-- person_keypoints_train2017.json
          |   |-- person_keypoints_val2017.json
          |   |-- image_info_test-dev2017.json
          `-- images
              |-- train2017
              |-- val2017
              |-- test2017
  

• [Optional] If you want to train ExtremeNet, generate extreme point annotation from segmentation:

    cd $CenterNet_ROOT/tools/
    python gen_coco_extreme_points.py
  

  It generates instances_extreme_train2017.json and instances_extreme_val2017.json in data/coco/annotations/.

Pascal VOC

• Run

    cd $CenterNet_ROOT/tools/
    bash download_pascal_voc.sh
  

• The above script includes:
  • Download, unzip, and move Pascal VOC images from the VOC website.
  • Download Pascal VOC annotation in COCO format (from Detectron).
  • Combine train/val 2007/2012 annotation files into a single json.

• Move the created voc folder to data (or create symlinks) to make the data folder like:

  ${CenterNet_ROOT}
  |-- data
  `-- |-- voc
      `-- |-- annotations
          |   |-- pascal_trainval0712.json
          |   |-- pascal_test2017.json
          |-- images
          |   |-- 000001.jpg
          |   ......
          `-- VOCdevkit
    
  

  The VOCdevkit folder is needed to run the evaluation script from faster rcnn.

KITTI

• Download images, annotations, and calibrations from KITTI website and unzip.

• Download the train-val split of 3DOP and SubCNN and place the data as below

  ${CenterNet_ROOT}
  |-- data
  `-- |-- kitti
      `-- |-- training
          |   |-- image_2
          |   |-- label_2
          |   |-- calib
          |-- ImageSets_3dop
          |   |-- test.txt
          |   |-- train.txt
          |   |-- val.txt
          |   |-- trainval.txt
          `-- ImageSets_subcnn
              |-- test.txt
              |-- train.txt
              |-- val.txt
              |-- trainval.txt
  

• Run python convert_kitti_to_coco.py in tools to convert the annotation into COCO format. You can set DEBUG=True in line 5 to visualize the annotation.

• Link image folder

  cd ${CenterNet_ROOT}/data/kitti/
  mkdir images
  ln -s training/image_2 images/trainval
  

• The data structure should look like:

  ${CenterNet_ROOT}
  |-- data
  `-- |-- kitti
      `-- |-- annotations
          |   |-- kitti_3dop_train.json
          |   |-- kitti_3dop_val.json
          |   |-- kitti_subcnn_train.json
          |   |-- kitti_subcnn_val.json
          `-- images
              |-- trainval
              |-- test
  

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