Introduction

RSI-Classification is an open source image classification toolbox based on PyTorch.

The master branch works with PyTorch 1.10+.

Thanks to openMMlab, we have the following

Major features

• Various backbones and pretrained models
• Bag of training tricks
• Large-scale training configs
• High efficiency and extensibility
• Powerful toolkits

Installation

Below are quick steps for installation:

conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
git clone https://github.com/EarthNets/RSI-Classification.git
cd RSI-Classification
pip3 install -e .

Getting Started

Coming soon

Model zoo

Results and models are available in the [model zoo].

Supported backbones

• VGG
• ResNet
• ResNeXt
• SE-ResNet
• SE-ResNeXt
• RegNet
• ShuffleNetV1
• ShuffleNetV2
• MobileNetV2
• MobileNetV3
• Swin-Transformer
• RepVGG
• Vision-Transformer
• Transformer-in-Transformer
• Res2Net
• MLP-Mixer
• DeiT
• Conformer
• T2T-ViT
• Twins
• EfficientNet
• ConvNeXt
• HRNet
• VAN
• ConvMixer
• CSPNet
• PoolFormer

Contributing

We appreciate all contributions to improve MMClassification. Please refer to CONTRUBUTING.md for the contributing guideline.

Acknowledgement

We thank the MMClassification. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new classifiers.

Citation

If you find this project useful in your research, please consider cite:

@article{earthnets4eo,
    title={EarthNets: Empowering AI in Earth Observation},
    author={Zhitong Xiong, Fahong Zhang, Yi Wang, Yilei Shi, Xiao Xiang Zhu},
    journal = {arXiv:2210.04936},
    year={2022}
}

License

This project is released under the Apache 2.0 license.