GHM_Detection

The implementation of Gradient Harmonized Single-stage Detector published on AAAI 2019 (Oral).

Updates

(May 24, 2019)

• Make mmdetection a submodule to keep it up-to-date.

Installation

This project is based on mmdetection.

Requirements

• Python 3.5+
• PyTorch 1.0+ (Based on the current version of mmdetection)
• CUDA 9.0+

Setup the Environment and Packages

i. Create a new environment We recommend Anaconda as the package & environment manager. And here is an example:

conda create -n ghm
conda activate ghm

ii. Install PyTorch Follow the official instructions to install Pytorch. Here is an example using conda:

conda install pytorch torchvision -c pytorch

iii. Install Cython

conda install cython 
# or "pip install cython"

Install GHM

i. Clone the repository

git clone --recursive https://github.com/libuyu/GHM_Detection.git

ii. Compile extensions

cd GHM_Detection/mmdetection

./compile.sh

iii. Setup mmdetection

pip install -e . 
# editable mode is convinient when debugging
# if your code in mmdetection is fixed, use "pip install ." directly

Prepare Data

It is recommended to symlink the datasets root to mmdetection/data.

ln -s $YOUR_DATA_ROOT data

The directories should be arranged like this:

GHM_detection
├──	mmdetection
|	├── mmdet
|	├── tools
|	├── configs
|	├── data
|	│   ├── coco
|	│   │   ├── annotations
|	│   │   ├── train2017
|	│   │   ├── val2017
|	│   │   ├── test2017
|	│   ├── VOCdevkit
|	│   │   ├── VOC2007
|	│   │   ├── VOC2012

Running

Script

We provide training and testing scripts and configuration files for both GHM and baseline (focal loss and smooth L1 loss) in the experiments directory. You need specify the path of your own pre-trained model in the config files.

Configuration

The configuration parameters are mainly in the cfg_*.py files. The parameters you most probably change are as follows:

• work_dir: the directory for current experiment
• datatype: data set name (coco, voc, etc.)
• data_root: Root for the data set
• model.pretrained: the path to the ImageNet pretrained backbone model
• resume_from: path or checkpoint file if resume
• train_cfg.ghmc: params for GHM-C loss
  • bins: unit region numbers
  • momentum: moving average parameter \alpha
• train_cfg.ghmr: params for GHM-R loss
  • mu: the \mu for ASL1 loss
  • bins, momentum: similar to ghmc
• total_epochs, lr_config.step: set the learning rate decay strategy

Loss Functions

• The GHM-C and GHM-R loss functions are available in ghm_loss.py.
• The code works for pytorch 1.0.1 and later version.

Result

Training using the Res50-FPN backbone and testing on COCO minival.

Method	AP
FL + SL1	35.6%
GHM-C + SL1	35.8%
GHM-C + GHM-R	37.0%

License

This project is released under the MIT license.

Citation

@inproceedings{li2019gradient,
  title={Gradient Harmonized Single-stage Detector},
  author={Li, Buyu and Liu, Yu and Wang, Xiaogang},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2019}
}

If the code helps you in your research, please also cite:

@article{mmdetection,
  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
  author  = {Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li,
             Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng,
             Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu,
             Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin},
  journal = {arXiv preprint arXiv:1906.07155},
  year    = {2019}
}