PyTorch implementation of our paper: Plug and Play Active Learning for Object Detection
- Our codebase is built on top of MMDetection, which can be installed following the offcial instuctions.
python setup.py install- 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.
- 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.
@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}
}