Dataset preparation

September 15, 2019 · View on GitHub

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
        |---|-- 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 get_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