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TODO ListVersion RecordInstallationAcknowledgementCitation


Open Source Remote Sensing Self-Supervised Learning. The repository is OPEN, Pull requests are welcome!

TODO List

  • Create Stable VISSL Version
  • Simplify Installation Process
  • Reduced Dependency Package
  • Add Actions
  • Complete CI/CD
  • Simplify Pretrain Config File [Add PROC_ID]
  • Complete the Pretrain Process
  • Check the VISSL Pretrain
  • Add Remote Sensing Image Segmentation Code
  • Add Remote Sensing Image Classfication Code
  • Add Remote Sensing Image Object Detection Code
  • Organize the Code with LangChain Style
  • Create Web Page
  • Create stable OpenRSSSL Version
  • Complete the Whole Process: From Pretrain to Specific-Task
  • Make Stable Fairscale

Version Record

2024-03-03 openrsssl (alpha version)

Installation

  1. Create Environment and Install Pytorch
conda create -n openrsssl_env python=3.8 -y
conda activate openrsssl_env
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia
  1. Install OpenRSSSL

Download OpenRSSSL source code and switch to the source path for installation:

pip install --progress-bar off -r requirements.txt
pip install -e .[dev]
  1. Optional: Install Apex from source
git clone --recursive https://www.github.com/NVIDIA/apex
cd apex
python3 setup.py install

Support Self-supervised Method

Supported Backbones General Self-supervised Learning Method Remote Sensing Self-supervised Learning Method
  • ResNet
  • ResNeXt
  • RegNet
  • MobileNet
  • Swin-Transformer
  • EfficientNet
  • ConvNeXt
  • BEiT
  • XCiT
    Contrastive:
  • SimCLR
  • MoCo
  • Barlow Twins
  • SwAV
  • Generative:
  • MSN
  • Self-Distillation:
  • DINO (DeiT)
  • iBOT (DeiT)
  • Coming soon:
  • BYOL *
  • DenseCL *
  • SimSiam *
  • MAE *
  • SimMIM *
  • EVA *
  • Deconstructing Denoising Diffusion Models for Self-Supervised Learning *
  • DINO v2
  • Jigsaw
    Coming soon:
  • GLCNet (TGRS) *
  • FALSE (GRSL) (coming soon.....)
  • GraSS (TGRS) (coming soon.....)

Acknowledgement

We would like to thank the VISSL and MMSegmentation for its open-source project.

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