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-# Grid-R-CNN
-
-An mmdetection based implementation of updated Grid R-CNN published on CVPR 2019. Original paper is [here](https://arxiv.org/abs/1811.12030), details of updates can be seen in this [technical report](update/updates_of_grcnn.pdf).
-
-## Installation
-
-This project is based on mmdetection object detection framework.
-
-### Requirements
-
-- Python 3.5+ ([Say goodbye to Python2](https://python3statement.org/))
-- PyTorch 1.0+ or PyTorch-nightly
-- CUDA 9.0+
-- GCC 4.9+
-- [mmcv](https://github.com/open-mmlab/mmcv)
-
-### Install Grid R-CNN with mmdetection
-
-a. Create a conda virtual environment and activate it. Then install Cython.
-
-```shell
-conda create -n open-mmlab python=3.7 -y
-source activate open-mmlab
-
-conda install cython
-```
-
-b. Install PyTorch stable or nightly and torchvision following the [official instructions](https://pytorch.org/).
-
-c. Clone the this repository.
-
-d. Compile cuda extensions.
-
-```shell
-./compile.sh
-```
-
-e. Setup mmdetection (other dependencies will be installed automatically).
-
-```shell
-python setup.py develop
-# or "pip install -e ."
-```
-
-### Dataset Preparation
-
-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
-
-```
-
-## Training and Testing
-
-We provide training and testing scripts and config files for Grid R-CNN. An example configuration file is [here](configs/grid_rcnn_r50_fpn_2x.py), corresponding checkpoint is [here](https://drive.google.com/file/d/1RCtNjb_JruBtl6sCq5w_XXN1e0ksn_f6/view?usp=sharing). 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](GETTING_STARTED.md) for more basic usage of mmdetection.
-
-## Results
-
-Training on Res50/101 FPN backbone and Testing on COCO minival. Inference speed is tested on TITANXp GPU cluster.
-
-Method |lr sched| AP | AP@0.5 | AP@0.75 | 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
-
-
-## Citation
-
-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}
-}
-
-@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}
-}
-```
-
-## License
-Grid R-CNN is released under the [Apache 2.0 license](https://github.com/STVIR/pysot/blob/master/LICENSE). 
-
-