An mmdetection based implementation of updated Grid R-CNN published on CVPR 2019. Original paper is here, details of updates can be seen in this technical report.
This project is based on mmdetection object detection framework.
- Python 3.5+ (Say goodbye to Python2)
- PyTorch 1.0+ or PyTorch-nightly
- CUDA 9.0+
- GCC 4.9+
- mmcv
a. Create a conda virtual environment and activate it. Then install Cython.
conda create -n open-mmlab python=3.7 -y
source activate open-mmlab
conda install cythonb. Install PyTorch stable or nightly and torchvision following the official instructions.
c. Clone the this repository.
d. Compile cuda extensions.
./compile.she. Setup mmdetection (other dependencies will be installed automatically).
python setup.py develop
# or "pip install -e ."It is recommended to symlink the dataset root to your project.
Grid_RCNN
├── mmdet
├── tools
├── configs
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
│ ├── VOCdevkit
│ │ ├── VOC2007
│ │ ├── VOC2012
We provide training and testing scripts and config files for Grid R-CNN. An example configuration file is here, corresponding checkpoint is here. Note that the batchsize we used is 64(32 GPUs/2 images per GPU), you could adjust the learning rate and warmup schedule according to your batchsize.
Please see GETTING_STARTED.md for more basic usage of mmdetection.
Training on Res50/101 FPN backbone and Testing on COCO minival. Inference speed is tested on our TITANXp GPU cluster.
| Method | lr sched | AP | [email protected] | [email protected] | AP@S | AP@M | AP@L | Inference speed |
|---|---|---|---|---|---|---|---|---|
| Res50-FPN | 2x | 37.4% | 59.3% | 40.3% | 21.8% | 40.9% | 47.9% | 0.09s |
| Res50-FPN-Grid R-CNN | 2x | 40.4% | 58.6% | 43.7% | 23.2% | 44.2% | 52.4% | 0.11s |
| Res101-FPN | 2x | 39.5% | 61.2% | 43.1% | 22.7% | 43.7% | 50.8% | 0.12s |
| Res101-FPN-Grid R-CNN | 2x | 42.0% | 60.6% | 45.4% | 24.1% | 46.2% | 55.2% | 0.13s |
If you use our codebase or models in your research, please cite this project.
@InProceedings{Lu_2019_CVPR,
author = {Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie},
title = {Grid R-CNN},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
@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}
}
Grid R-CNN is released under the Apache 2.0 license.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent