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AdelaiDet is an open source toolbox for multiple instance-level recognition tasks on top of Detectron2. All instance-level recognition works from our group are open-sourced here.
To date, AdelaiDet implements the following algorithms:
| Name | inf. time | box AP | download |
|---|---|---|---|
| FCOS_R_50_1x | 16 FPS | 38.7 | model |
| FCOS_MS_R_101_2x | 12 FPS | 43.1 | model |
| FCOS_MS_X_101_32x8d_2x | 6.6 FPS | 43.9 | model |
| FCOS_MS_X_101_32x8d_dcnv2_2x | 4.6 FPS | 46.6 | model |
| FCOS_RT_MS_DLA_34_4x_shtw | 52 FPS | 39.1 | model |
More models can be found in FCOS README.md.
| Model | Name | inf. time | box AP | mask AP | download |
|---|---|---|---|---|---|
| Mask R-CNN | R_101_3x | 10 FPS | 42.9 | 38.6 | |
| BlendMask | R_101_3x | 11 FPS | 44.8 | 39.5 | model |
| BlendMask | R_101_dcni3_5x | 10 FPS | 46.8 | 41.1 | model |
For more models and information, please refer to BlendMask README.md.
| Name | inf. time | box AP | mask AP | download |
|---|---|---|---|---|
| MEInst_R_50_3x | 12 FPS | 43.6 | 34.5 | model |
For more models and information, please refer to MEInst README.md.
| Name | inf. time | e2e-hmean | det-hmean | download |
|---|---|---|---|---|
| attn_R_50 | 11 FPS | 67.1 | 86.0 | model |
For more models and information, please refer to ABCNet README.md.
| Name | inf. time | box AP | mask AP | download |
|---|---|---|---|---|
| CondInst_MS_R_50_1x | 14 FPS | 39.7 | 35.7 | model |
| CondInst_MS_R_50_BiFPN_3x_sem | 13 FPS | 44.7 | 39.4 | model |
| CondInst_MS_R_101_3x | 11 FPS | 43.3 | 38.6 | model |
| CondInst_MS_R_101_BiFPN_3x_sem | 10 FPS | 45.7 | 40.2 | model |
For more models and information, please refer to CondInst README.md.
Note that:
First install Detectron2 following the official guide: INSTALL.md.
Please use Detectron2 with commit id 9eb4831 if you have any issues related to Detectron2.
Then build AdelaiDet with:
git clone https://github.com/aim-uofa/AdelaiDet.git
cd AdelaiDet
python setup.py build develop
If you are using docker, a pre-built image can be pulled with:
docker pull tianzhi0549/adet:latest
Some projects may require special setup, please follow their own README.md in configs.
fcos_R_50_1x.yaml.wget https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download -O fcos_R_50_1x.pth
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:
OMP_NUM_THREADS=1 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
To evaluate the model after training, run:
OMP_NUM_THREADS=1 python tools/train_net.py \
--config-file configs/FCOS-Detection/R_50_1x.yaml \
--eval-only \
--num-gpus 8 \
OUTPUT_DIR training_dir/fcos_R_50_1x \
MODEL.WEIGHTS training_dir/fcos_R_50_1x/model_final.pth
Note that:
--num-gpus.--num-gpus to 1.OMP_NUM_THREADS=1 by default, which achieves the best speed on our machines, please change it as needed.README.md in configs.The authors are grateful to Nvidia, Huawei Noah's Ark Lab, ByteDance, Adobe who generously donated GPU computing in the past a few years.
If you use this toolbox in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:
@misc{tian2019adelaidet,
author = {Tian, Zhi and Chen, Hao and Wang, Xinlong and Liu, Yuliang and Shen, Chunhua},
title = {{AdelaiDet}: A Toolbox for Instance-level Recognition Tasks},
howpublished = {\url{https://git.io/adelaidet}},
year = {2019}
}
@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}
}
@inproceedings{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},
booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}
@inproceedings{zhang2020MEInst,
title = {Mask Encoding for Single Shot Instance Segmentation},
author = {Zhang, Rufeng and Tian, Zhi and Shen, Chunhua and You, Mingyu and Yan, Youliang},
booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}
@inproceedings{liu2020abcnet,
title = {{ABCNet}: Real-time Scene Text Spotting with Adaptive {B}ezier-Curve Network},
author = {Liu, Yuliang and Chen, Hao and Shen, Chunhua and He, Tong and Jin, Lianwen and Wang, Liangwei},
booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}
@inproceedings{wang2020solo,
title = {{SOLO}: Segmenting Objects by Locations},
author = {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei},
booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)},
year = {2020}
}
@inproceedings{wang2020solov2,
title = {{SOLOv2}: Dynamic and Fast Instance Segmentation},
author = {Wang, Xinlong and Zhang, Rufeng and Kong, Tao and Li, Lei and Shen, Chunhua},
booktitle = {Proc. Advances in Neural Information Processing Systems (NeurIPS)},
year = {2020}
}
@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}
}
@inproceedings{tian2020conditional,
title = {Conditional Convolutions for Instance Segmentation},
author = {Tian, Zhi and Shen, Chunhua and Chen, Hao},
booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)},
year = {2020}
}
For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact Chunhua Shen.
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