Plug and Play Active Learning for Object Detection

PyTorch implementation of our paper: Plug and Play Active Learning for Object Detection

Requirements

• Our codebase is built on top of MMDetection, which can be installed following the offcial instuctions.

Usage

Installation

python setup.py install

Setup dataset

• Place your dataset as the following structure (Only vital files are shown). It should be easy because it's the default MMDetection data placement)

  PPAL
  |
  `-- data
  |
  |--coco
  |   |
  |   |--train2017
  |   |--val2017
  |   `--annotations
  |      |
  |      |--instances_train2017.json
  |      `--instances_val2017.json
  `-- VOCdevkit
      |
      |--VOC2007
      |  |
      |  |--ImageSets
      |  |--JPEGImages
      |  `--Annotations
      `--VOC2012
         |--ImageSets
         |--JPEGImages
         `--Annotations

• For convenience, we use COCO style annotation for Pascal VOC active learning. Please download trainval_0712.json.
• Set up active learning datasets

  zsh tools/al_data/data_setup.sh /path/to/trainval_0712.json

• The above command will set up a new Pascal VOC data folder. It will also generate three different active learning initial annotations for both dataset, where the COCO initial sets contain 2% of the original annotated images, and the Pascal VOC initial sets contains 5% of the original annotated images.
• The resulted file structure is as following

  PPAL
  |
  `-- data
  |
  |--coco
  |   |
  |   |--train2017
  |   |--val2017
  |   `--annotations
  |      |
  |      |--instances_train2017.json
  |      `--instances_val2017.json
  |--VOCdevkit
  |   |
  |   |--VOC2007
  |   |  |
  |   |  |--ImageSets
  |   |  |--JPEGImages
  |   |  `--Annotations
  |   `--VOC2012
  |      |--ImageSets
  |      |--JPEGImages
  |      `--Annotations
  |--VOC0712
  |  |
  |  |--images
  |  |--annotations
  |     |
  |     `--trainval_0712.json
  `--active_learning
     |
     |--coco
     |  |
     |  |--coco_2365_labeled_1.json
     |  |--coco_2365_unlabeled_1.json
     |  |--coco_2365_labeled_2.json
     |  |--coco_2365_unlabeled_2.json
     |  |--coco_2365_labeled_3.json
     |  `--coco_2365_unlabeled_3.json
     `--voc
        |
        |--voc_827_labeled_1.json
        |--voc_827_unlabeled_1.json
        |--voc_827_labeled_2.json
        |--voc_827_unlabeled_2.json
        |--voc_827_labeled_3.json
        `--voc_827_unlabeled_3.json

• Please refer to data_setup.sh and create_al_dataset.py to generate you own active learning annotation.

  Run active learning

• You can run active learning using a single command with a config file. For example, you can run COCO and Pascal VOC RetinaNet experiments by

  python tools/run_al_coco.py --config al_configs/coco/ppal_retinanet_coco.py --model retinanet
  python tools/run_al_voc.py --config al_configs/voc/ppal_retinanet_voc.py --model retinanet

• Please check the config file to set up the data paths and environment settings before running the experiments.

  Citation

@InProceedings{yang2024ppal,
    author    = {{Yang, Chenhongyi and Huang, Lichao and Crowley, Elliot J.}},
    title     = {{Plug and Play Active Learning for Object Detection}},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2024}
}