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 to be released
• ABCNet to be released (demo)
• SOLO to be released (mmdet version)
• SOLOv2 to be released (mmdet version)
• DirectPose to be released
• CondInst to be released

Models

All of our trained models are available in the Model Zoo.

COCO Object Detecton Baselines with FCOS

Name	box AP	download
FCOS_R_50_1x	38.7	model

COCO Instance Segmentation Baselines with BlendMask

Model	Name	inference time (ms/im)	box AP	mask AP	download
Mask R-CNN	550_R_50_3x	63	39.1	35.3
BlendMask	550_R_50_3x	36	38.7	34.5	model
Mask R-CNN	R_50_1x	80	38.6	35.2
BlendMask	R_50_1x	73	39.9	35.8	model
Mask R-CNN	R_50_3x	80	41.0	37.2
BlendMask	R_50_3x	74	42.7	37.8	model
Mask R-CNN	R_101_3x	100	42.9	38.6
BlendMask	R_101_3x	94	44.8	39.5	model
BlendMask	R_101_dcni3_5x	105	46.8	41.1	model

Installation

First install Detectron2 following the official guide: INSTALL.md. Then build AdelaiDet with:

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

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:

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:

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

The configs are made for 8-GPU training. To train on another number of GPUs, change the num-gpus.

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{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}
}

@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{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}
}

@article{tian2020conditional,
  title   = {Conditional Convolutions for Instance Segmentation},
  author  = {Tian, Zhi and Shen, Chunhua and Chen, Hao},
  journal = {arXiv preprint arXiv:2003.05664},
  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.