AdelaiDet

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:

• FCOS
• BlendMask
• MEInst
• ABCNet
• CondInst
• SOLO to be released (mmdet version)
• SOLOv2 to be released (mmdet version)
• DirectPose to be released

Models

COCO Object Detecton Baselines with FCOS

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.

COCO Instance Segmentation Baselines with BlendMask

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.

COCO Instance Segmentation Baselines with MEInst

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.

Total_Text results with ABCNet

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.

COCO Instance Segmentation Baselines with CondInst

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:

• Inference time for all projects is measured on a NVIDIA 1080Ti with batch size 1.
• APs are evaluated on COCO2017 val split unless specified.

Installation

First install Detectron2 following the official guide: INSTALL.md. Please use Detectron2 with commit id 45a4d97 for now. The incompatibility with the latest one will be fixed soon.

Then build AdelaiDet with:

git clone https://github.com/aim-uofa/AdelaiDet.git
cd AdelaiDet
python setup.py build develop

Some projects may require special setup, please follow their own README.md in configs.

Quick Start

Inference with Pre-trained Models

1. Pick a model and its config file, for example, fcos_R_50_1x.yaml.
2. Download the model wget https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download -O fcos_R_50_1x.pth
3. 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

Train Your Own Models

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:

• The configs are made for 8-GPU training. To train on another number of GPUs, change the --num-gpus.
• If you want to measure the inference time, please change --num-gpus to 1.
• We set OMP_NUM_THREADS=1 by default, which achieves the best speed on our machines, please change it as needed.
• This quick start is made for FCOS. If you are using other projects, please check the projects' own README.md in configs.

Citing AdelaiDet

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}
}

@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 Bezier-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}
}

@article{wang2020solov2,
  title   =  {{SOLOv2}: Dynamic, Faster and Stronger},
  author  =  {Wang, Xinlong and Zhang, Rufeng and Kong, Tao and Li, Lei and Shen, Chunhua},
  journal =  {arXiv preprint arXiv:2003.10152},
  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}
}

License

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.