Awesome
MMDetection For Remote Sensing
News: This project is base on mmdetection to reimplement RRPN and use the model Faster R-CNN OBB
Introduction
The master branch works with PyTorch 1.1 or higher.
mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.
Benchmark and model zoo
Supported methods and backbones are shown in the below table. Results and models are available in the Model zoo.
| ResNet | ResNeXt | SENet | VGG | HRNet | |
|---|---|---|---|---|---|
| RPN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Fast R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Faster R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Mask R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Cascade R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Cascade Mask R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| SSD | ✗ | ✗ | ✗ | ✓ | ✗ |
| RetinaNet | ✓ | ✓ | ☐ | ✗ | ✓ |
| GHM | ✓ | ✓ | ☐ | ✗ | ✓ |
| Mask Scoring R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| FCOS | ✓ | ✓ | ☐ | ✗ | ✓ |
| Double-Head R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Grid R-CNN (Plus) | ✓ | ✓ | ☐ | ✗ | ✓ |
| Hybrid Task Cascade | ✓ | ✓ | ☐ | ✗ | ✓ |
| Libra R-CNN | ✓ | ✓ | ☐ | ✗ | ✓ |
| Guided Anchoring | ✓ | ✓ | ☐ | ✗ | ✓ |
Other features
- DCNv2
- Group Normalization
- Weight Standardization
- OHEM
- Soft-NMS
- Generalized Attention
- GCNet
- Mixed Precision (FP16) Training
Installation
- Please refer to INSTALL.md for installation and dataset preparation.
- Before install, you should make sure the configuration is correct
vim ~/.condarc
channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
show_channel_urls: true
vim ~/.bashrc
export GCCPATH=/mnt/lustre/share/gcc/gcc-5.3.0
export PATH=$GCCPATH/bin:$PATH
export CC=$GCCPATH/bin/gcc
export CXX=$GCCPATH/bin/g++
export LD_LIBRARY_PATH=$GCCPATH/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/mnt/lustre/share/gcc/gmp-4.3.2/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/mnt/lustre/share/gcc/mpc-0.8.1/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/mnt/lustre/share/gcc/mpfr-2.4.2/lib:$LD_LIBRARY_PATH
export CUDA_HOME=/mnt/lustre/share/cuda-9.0
export PATH=$CUDA_HOME/bin:$PATH
export PATH=/mnt/lustre/share/cuda-9.0/lib64/libcudnn.so.7.0.4::$PATH
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64
export LIBRARY_PATH=$LIBRARY_PATH:$CUDA_HOME/lib64
- You can install directly from the script below
export INSTALL_DIR=$PWD
conda create -n open-mmlab python=3.7 -y
source activate open-mmlab
conda install pytorch torchvision==0.2.2 cuda90 cudatoolkit=9.0 -y
conda install cython -y
cd $INSTALL_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext
cd $INSTALL_DIR
git clone git@gitlab.bj.sensetime.com:yanhongchang/mmdetection.git
cd mmdetection
git checkout rotated
python setup.py build develop
python setup_rotated.py build develop
unset INSTALL_DIR
rm -rf /mnt/lustre/yanhongchang/.conda/envs/open-mmlab/lib/python3.7/site-packages/torchvision-0.4.1-py3.7-linux-x86_64.egg/
Get Started
Please see GETTING_STARTED.md for the basic usage of MMDetection.