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
• 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	inf. time	box AP	download
FCOS_R_50_1x	16 FPS	38.7	model
FCOS_MS_R_50_2x	16 FPS	41.0	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_64x4d_2x	6.1 FPS	44.7	model
FCOS_MS_X_101_32x8d_dcnv2_2x	4.6 FPS	46.6	model

Except for FCOS_R_50_1x, all other models are trained with multi-scale data augmentation.

FCOS Real-time Models

Name	inf. time	box AP	download
FCOS_RT_MS_DLA_34_4x_shtw	52 FPS	39.1	model
FCOS_RT_MS_DLA_34_4x	46 FPS	40.3	model
FCOS_RT_MS_R_50_4x	38 FPS	40.2	model

If you prefer BN in FCOS heads, please try the following models.

Name	inf. time	box AP	download
FCOS_RT_MS_DLA_34_4x_shtw_bn	52 FPS	38.9	model
FCOS_RT_MS_DLA_34_4x_bn	48 FPS	39.4	model
FCOS_RT_MS_R_50_4x_bn	40 FPS	39.3	model

Inference time is measured on a NVIDIA 1080Ti with batch size 1. Real-time models use shorter side 512 for inference.

COCO Instance Segmentation Baselines with BlendMask

Model	Name	inf. time	box AP	mask AP	download
Mask R-CNN	550_R_50_3x	16FPS	39.1	35.3
BlendMask	550_R_50_3x	28FPS	38.7	34.5	model
BlendMask	RT_R_50_4x	30FPS	40.1	34.6	model
BlendMask	DLA_34_4x	32FPS	40.9	35.2	model
Mask R-CNN	R_50_1x	13FPS	38.6	35.2
BlendMask	R_50_1x	14FPS	39.9	35.8	model
Mask R-CNN	R_50_3x	13FPS	41.0	37.2
BlendMask	R_50_3x	14FPS	42.7	37.8	model
Mask R-CNN	R_101_3x	10FPS	42.9	38.6
BlendMask	R_101_3x	11FPS	44.8	39.5	model
BlendMask	R_101_dcni3_5x	10FPS	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:

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

• 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.

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.