GHM_Detection

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

Installation

This project is based on mmdetection. And we add GHM losses in it and modify some code for compatibility.

Requirements

Python 3.4+
PyTorch 0.4.1+ (We have the pytorch-0.3 branch for 0.3.x version, but it is no longer updated)

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 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 0.4.1 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:

@misc{mmdetection2018,
  author =       {Kai Chen and Jiangmiao Pang and Jiaqi Wang and Yu Xiong and Xiaoxiao Li
                  and Shuyang Sun and Wansen Feng and Ziwei Liu and Jianping Shi and
                  Wanli Ouyang and Chen Change Loy and Dahua Lin},
  title =        {mmdetection},
  howpublished = {\url{https://github.com/open-mmlab/mmdetection}},
  year =         {2018}
}

giorking / GHM_Detection