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dfc2025track1

The official implementation for the 1st-place winner solution of GRSS DFC 2025 track1 'All Wheather Land Cover Mapping'

https://github.com/stuliu/dfc2025track1

Science Score: 26.0%

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    CITATION.cff file
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    Low similarity (8.6%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

The official implementation for the 1st-place winner solution of GRSS DFC 2025 track1 'All Wheather Land Cover Mapping'

Basic Info
  • Host: GitHub
  • Owner: StuLiu
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 43.8 MB
Statistics
  • Stars: 7
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

Code for GRSS DFC 2025 track1.

Docker

Alternatively, you could pull the docker image and test. bash sudo su docker pull registry.ap-northeast-1.aliyuncs.com/liuwang20144623/dfc2025track1:v1 docker images docker run -it --shm-size=60g --gpus all [image_id] /bin/bash cd /workspace/DFC2025Track1 pip install mmpretrain bash run_report.sh

or testing in local env:

1.Conda env

Cuda version is 11.8. commandline conda create -n mmseg_dfc python=3.10 source activate mmseg_dfc pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 pip install mmcv=2.1.0 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.1/index.html pip install mmengine==0.10.2 cd src/causal-conv1d-1.0.2 pip install -e . cd ../mamba-1.0.1 pip install -e . cd ../.. pip install -e . pip install ttach pip install kornia pip install ftfy pip install scikit-image pip install timm pip install mmpretrain

2.Data prepare

Please see in file 'data/DFC2025Track1/copydatasethere'.

  • If testing only, please copy the OEM-SAR test images to 'data/DFC2025Track1/test/sar_images'.
  • If training, the open-earth-map (OEM) and open-erath-map-SAR (OEM-SAR) are utilized to train our method. \ Please reorganize the dir tree as shown in 'data/copydatasethere'.

3.Test

Reproduce similarity results: The trained model weights should be downloaded here, password=433w commandline bash run_report.sh Note that the final results is slightly different due to the various type of GPUs.

4.Train

You can also find the training pipline for a single model in 'run_pipline.sh':`

commandline bash run_pipline.sh

Owner

  • Name: Wang Liu
  • Login: StuLiu
  • Kind: user
  • Company: Hunan University, China

GitHub Events

Total
  • Issues event: 1
  • Watch event: 9
  • Issue comment event: 2
  • Push event: 2
  • Fork event: 1
  • Create event: 2
Last Year
  • Issues event: 1
  • Watch event: 9
  • Issue comment event: 2
  • Push event: 2
  • Fork event: 1
  • Create event: 2

Dependencies

.github/workflows/deploy.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
dinov2/dinov2/eval/setup.py pypi
dinov2/pyproject.toml pypi
dinov2/requirements-dev.txt pypi
  • black ==22.6.0 development
  • flake8 ==5.0.4 development
  • pylint ==2.15.0 development
dinov2/requirements-extras.txt pypi
  • mmcv-full ==1.5.0
  • mmsegmentation ==0.27.0
dinov2/requirements.txt pypi
  • cuml-cu11 *
  • fvcore *
  • iopath *
  • omegaconf *
  • submitit *
  • torch ==2.0.0
  • torchmetrics ==0.10.3
  • torchvision ==0.15.0
  • xformers ==0.0.18
dinov2/setup.py pypi
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx_copybutton *
  • sphinx_markdown_tables *
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.5.0,<1.0.0
requirements/multimodal.txt pypi
  • ftfy *
  • regex *
requirements/optional.txt pypi
  • cityscapesscripts *
  • diffusers *
  • einops ==0.3.0
  • imageio ==2.9.0
  • imageio-ffmpeg ==0.4.2
  • invisible-watermark *
  • kornia ==0.6
  • nibabel *
  • omegaconf ==2.1.1
  • pudb ==2019.2
  • pytorch-lightning ==1.4.2
  • streamlit >=0.73.1
  • test-tube >=0.7.5
  • timm *
  • torch-fidelity ==0.3.0
  • torchmetrics ==0.6.0
  • transformers ==4.19.2
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc1,<2.1.0
  • mmengine >=0.4.0,<1.0.0
  • prettytable *
  • scipy *
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • packaging *
  • prettytable *
  • scipy *
requirements/tests.txt pypi
  • codecov * test
  • flake8 * test
  • ftfy * test
  • interrogate * test
  • pytest * test
  • regex * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements.txt pypi
  • ftfy *
  • kornia *
  • scikit-image *
  • ttach *
setup.py pypi
src/causal-conv1d-1.0.2/setup.py pypi
  • buildtools *
  • ninja *
  • packaging *
  • torch *
src/mamba-1.0.1/setup.py pypi
  • causal_conv1d *
  • einops *
  • ninja *
  • packaging *
  • torch *
  • transformers *
  • triton *