We hope to integrate remote sensing related work based on MMLab, especially MMDetection and MMRotate.
Real Time
| Rotated YOLOX (arXiv 2021) | RTMDet (arXiv 2022) | Rotated YOLOMS (TPAMI 2025) | RTDETR (CVPR' 2024) |
| O2-RTDETR (TGRS 2026) |
Oriented Object Detection - Architecture
Oriented Object Detection - Loss
| GWD (ICML'2021) |
KLD (NeurIPS'2021) |
KFIoU (ICLR'2023) |
Oriented Object Detection - Coder
| CSL (ECCV'2020) |
Oriented R-CNN (ICCV'2021) |
PSC (CVPR'2023) |
ACM (CVPR'2024) |
GauCho (CVPR'2025) |
Oriented Object Detection - Backbone
Oriented Object Detection - Weakly Supervise
| H2RBox (ICLR'2023) |
H2RBox-v2 (Nips'2023) |
Point2Rbox (CVPR'2024) |
Point2Rbox-v2 (CVPR'2025) |
WhollyWOOD (TPAMI'2025) |
| ABBSPO (CVPR'2025) |
Oriented Object Detection - Semi Supervise
Coming soon
SAM
| MMRotate SAM |
Scene graph generation (SGG)
| STAR (TPAMI'2025) | ReCon1M (TGRS'2025) |
Change Detection
| FC-EF (ICIP'2018) | FC-Siam-conc (ICIP'2018) | FC-Siam-diff (ICIP'2018) | Changer (TGRS'2023) |
| BiT (TGRS' 2021) | IDA-SiamNet (TGRS' 2024) |
Instance Segmentation
| CATNet (TNNLS'2024) |
To support H2rbox_v2, point2rbox, and mamba, we use pytorch-2.x.
Step 1: Install Anaconda or Miniconda
Step 2: Create a virtual environment
conda create --name ai4rs python=3.10 -y
conda activate ai4rs
Step 3: Install Pytorch according to official instructions. For example:
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121
Verify whether pytorch supports cuda
python -c "import torch; print(torch.cuda.is_available())"
Step 4: Install MMEngine and MMCV, and we recommend using MIM to complete the installation
pip install -U openmim -i https://pypi.tuna.tsinghua.edu.cn/simple
mim install mmengine -i https://pypi.tuna.tsinghua.edu.cn/simple
mim install "mmcv==2.2.0" -i https://pypi.tuna.tsinghua.edu.cn/simple
Step 5: Install MMDetection and MMSegmentation(for change detection)
mim install 'mmdet>3.0.0rc6, <3.4.0' -i https://pypi.tuna.tsinghua.edu.cn/simple
mim install "mmsegmentation>=1.2.2" -i https://pypi.tuna.tsinghua.edu.cn/simple
Change the version number in the mmdet code.
python -c "import mmdet; print(mmdet.__file__)"
# ...
# File "/root/anaconda3/envs/ai4rs/lib/python3.10/site-packages/mmdet/__init__.py", line 17,
# in <module> and mmcv_version < digit_version(mmcv_maximum_version)), \
# AssertionError: MMCV==2.2.0 is used but incompatible. Please install mmcv>=2.0.0rc4, <2.2.0.
modify /root/anaconda3/envs/ai4rs/lib/python3.10/site-packages/mmdet/__init__.py
vim '/root/anaconda3/envs/ai4rs/lib/python3.10/site-packages/mmdet/__init__.py'
# mmcv_maximum_version = '2.2.0' -> '2.3.0'
Change the version number in the mmseg code.
python -c "import mmseg; print(mmseg.__file__)"
# ...
# File "/root/anaconda3/envs/ai4rs/lib/python3.10/site-packages/mmseg/__init__.py", line 61,
# in <module> assert (mmcv_min_version <= mmcv_version < mmcv_max_version), \
# AssertionError: MMCV==2.2.0 is used but incompatible. Please install mmcv>=2.0.0rc4.
modify /root/anaconda3/envs/ai4rs/lib/python3.10/site-packages/mmseg/__init__.py
vim '/root/anaconda3/envs/ai4rs/lib/python3.10/site-packages/mmseg/__init__.py'
# MMCV_MAX = '2.2.0' -> '2.3.0'
Step 6: Install ai4rs
git clone https://github.com/wokaikaixinxin/ai4rs.git
cd ai4rs
Option 1: Basic Installation
Use this for a lightweight setup with only the core functional dependencies.
pip install -v -e . -i https://pypi.tuna.tsinghua.edu.cn/simple
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.
Option 2: Full Installation (Recommended)
Use this if you want all features and dependencies (e.g., change detection).
pip install -v -e .[all] -i https://pypi.tuna.tsinghua.edu.cn/simple
Please refer to data_preparation.md to prepare the data
Single-node single-GPU
python tools/train.py config_path
For example:
python tools/train.py projects/GSDet_baseline/configs/GSDet_r50_b900_h2h4_h2r1_r2r1_2x_dior.py
Single-node multi-GPU
bash tools/dist_train.sh config_path num_gpus
For example:
bash tools/dist_train.sh projects/GSDet_baseline/configs/GSDet_r50_b900_h2h4_h2r1_r2r1_2x_dior.py 2
Single-node single-GPU
python tools/test.py config_path checkpoint_path
For example:
python tools/test.py configs/h2rbox_v2/h2rbox_v2-le90_r50_fpn-1x_dota.py work_dirs/h2rbox_v2-le90_r50_fpn-1x_dota-fa5ad1d2.pth
Single-node multi-GPU
bash tools/dist_test.sh config_path checkpoint_path num_gpus
For example:
bash tools/dist_test.sh configs/h2rbox_v2/h2rbox_v2-le90_r50_fpn-1x_dota.py work_dirs/h2rbox_v2-le90_r50_fpn-1x_dota-fa5ad1d2.pth 2
Please see Overview for the general introduction of Openmmlab.
For detailed user guides and advanced guides, please refer to our documentation:
Please refer to FAQ for frequently asked questions.
If you use this toolbox or benchmark in your research, please cite this project ai4rs