Active Learning with Core-set Sampling and Scale-sensitive Loss for 3D Object Detection

Requirements

Installation

• Linux
• Python 3.6+
• PyTorch 1.1 or higher
• CUDA 9.0 or higher

Please refer to INSTALL.md for the installation.

Config

The dataset configs are located within tools/cfgs/dataset_configs, and the model configs are located within tools/cfgs for different datasets.

Getting Started

• Download the offical KITTI 3D Object Detection Dataset and organize as follows:

  AL4OD
  ├── data
  │   ├── kitti
  │   │   │── ImageSets
  │   │   │── training
  │   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
  │   │   │── testing
  │   │   │   ├──calib & velodyne & image_2
  ├── pcdet
  ├── tools
  

• Generate Info as follows:

  python -m pcdet.datasets.kitti.kitti_dataset_AL create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset_AL.yaml

Training

• Run following command to start training.

  python train_AL.py --cfg_file ${CONFIG_FILE}

Acknowledgement

Our implementation is mainly based on OpenPCDet, thanks for their wonderful work.