/AdelaiDet

AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

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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:

Models

More models will be released soon. Stay tuned.

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.

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.