AdelaiDet is an open source toolbox for multiple instance-level detection applications based on Detectron2. All instance-level recognition works from our group will be open-sourced here.
AdelaiDet will inlcude the following algorithms:
More models will be released soon.
| Name | box AP | download |
|---|---|---|
| FCOS_R_50_1x | 38.7 | model |
First install Detectron2 following the official guide: INSTALL.md. Then build AdelaiDet with:
git clone https://github.com/aim-uofa/adet.git
cd adet
python setup.py build develop
- Pick a model and its config file, for example,
fcos_R_50_1x.yaml. - Download the model
wget https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download -O fcos_R_50_1x.pth - Run the demo with
python demo/demo.py \
--config-file configs/FCOS-Detection/R_50_1x.yaml \
--input input1.jpg input2.jpg \
--opts MODEL.WEIGHTS fcos_R_50_1x.pth
To train a model with "train_net.py", first setup the corresponding datasets following datasets/README.md, then run:
python tools/train_net.py \
--config-file configs/FCOS-Detection/R_50_1x.yaml \
--num-gpus 8 \
OUTPUT_DIR training_dir/fcos_R_50_1x
The configs are made for 8-GPU training. To train on another number of GPUs, change the num-gpus.
If you use this toolbox in your research or wish to refer to the baseline results, please use the following BibTeX entries.
@inproceedings{tian2019fcos,
title = {{FCOS}: Fully Convolutional One-Stage Object Detection},
author = {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
booktitle = {Proc. Int. Conf. Computer Vision (ICCV)},
year = {2019}
}
@article{chen2020blendmask,
title = {BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation},
author = {Chen, Hao and Sun, Kunyang and Tian, Zhi and Shen, Chunhua and Huang, Yongming and Yan, Youliang},
journal = {arXiv preprint arXiv:2001.00309},
year = {2020}
}
@article{wang2019solo,
title = {SOLO: Segmenting Objects by Locations},
author = {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei},
journal = {arXiv preprint arXiv:1912.04488},
year = {2019}
}
@article{tian2019directpose,
title = {DirectPose: Direct End-to-End Multi-Person Pose Estimation},
author = {Tian, Zhi and Chen, Hao and Shen, Chunhua},
journal = {arXiv preprint arXiv:1911.07451},
year = {2019}
}For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact the authors.
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