Skip to content
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
Python Cuda C++
Branch: master
Clone or download

Latest commit

Latest commit 0f2d3c1 Apr 1, 2020

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
adet add readthedoc support Feb 26, 2020
configs/FCOS-Detection move vovnet configs to FCOS-Detection Feb 18, 2020
demo initial commit Jan 23, 2020
docs add cuda detectron2 as dependencies Feb 26, 2020
tools initial commit Jan 23, 2020
.gitignore initial commit Jan 23, 2020
LICENSE initial commit Jan 23, 2020
README.md Update README.md Apr 1, 2020
setup.py add readthedoc support Feb 26, 2020

README.md

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 box AP download
FCOS_R_50_1x 38.7 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.

You can’t perform that action at this time.