Awesome

OpenDet

Expanding Low-Density Latent Regions for Open-Set Object Detection (CVPR2022)<br> Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-Song Xia.<br> arXiv preprint.

OpenDet2: OpenDet is implemented based on detectron2.

Setup

The code is based on detectron2 v0.5.

Installation

Here is a from-scratch setup script.

conda create -n opendet2 python=3.8 -y
conda activate opendet2

conda install pytorch=1.8.1 torchvision cudatoolkit=10.1 -c pytorch -y
pip install detectron2==0.5 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html
git clone https://github.com/csuhan/opendet2.git
cd opendet2
pip install -v -e .

Prepare datasets

Please follow datasets/README.md for dataset preparation. Then we generate VOC-COCO datasets.

bash datasets/opendet2_utils/prepare_openset_voc_coco.sh
# using data splits provided by us.
cp datasets/voc_coco_ann datasets/voc_coco -rf

Model Zoo

We report the results on VOC and VOC-COCO-20, and provide pretrained models. Please refer to the corresponding log file for full results.

Faster R-CNN

Method	backbone	mAP<sub>K↑</sub>(VOC)	WI<sub>↓</sub>	AOSE<sub>↓</sub>	mAP<sub>K↑</sub>	AP<sub>U↑</sub>	Download
FR-CNN	R-50	80.06	19.50	16518	58.36	0	config model
PROSER	R-50	79.42	20.44	14266	56.72	16.99	config model
ORE	R-50	79.80	18.18	12811	58.25	2.60	config model
DS	R-50	79.70	16.76	13062	58.46	8.75	config model
OpenDet	R-50	80.02	12.50	10758	58.64	14.38	config model
OpenDet	Swin-T	83.29	10.76	9149	63.42	16.35	config model

RetinaNet

Method	mAP<sub>K↑</sub>(VOC)	WI<sub>↓</sub>	AOSE<sub>↓</sub>	mAP<sub>K↑</sub>	AP<sub>U↑</sub>	Download
RetinaNet	79.63	14.16	36531	57.32	0	config model
Open-RetinaNet	79.64	10.74	17208	57.32	10.55	config model

Note:

You can also download the pre-trained models at github release or BaiduYun with extracting code ABCD.
The above results are reimplemented. Therefore, they are slightly different from our paper.
The official code of ORE is at OWOD. So we do not plan to include ORE in our code.

Online Demo

Try our online demo at huggingface space.

Train and Test

Testing

First, you need to download pretrained weights in the model zoo, e.g., OpenDet.

Then, run the following command:

python tools/train_net.py --num-gpus 8 --config-file configs/faster_rcnn_R_50_FPN_3x_opendet.yaml \
        --eval-only MODEL.WEIGHTS output/faster_rcnn_R_50_FPN_3x_opendet/model_final.pth

Training

The training process is the same as detectron2.

python tools/train_net.py --num-gpus 8 --config-file configs/faster_rcnn_R_50_FPN_3x_opendet.yaml

To train with the Swin-T backbone, please download swin_tiny_patch4_window7_224.pth and convert it to detectron2's format using tools/convert_swin_to_d2.py.

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
python tools/convert_swin_to_d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224_d2.pth

Citation

If you find our work useful for your research, please consider citing:

@InProceedings{han2022opendet,
    title     = {Expanding Low-Density Latent Regions for Open-Set Object Detection},
    author    = {Han, Jiaming and Ren, Yuqiang and Ding, Jian and Pan, Xingjia and Yan, Ke and Xia, Gui-Song},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2022}
}