Initial version.
Results and models are available in the [model zoo].
Supported datasets:
Continually updating
Supported backbones:
- ResNet (CVPR'2016)
- ResNeXt (CVPR'2017)
- HRNet (CVPR'2019)
- ResNeSt (ArXiv'2020)
- MobileNetV2 (CVPR'2018)
- MobileNetV3 (ICCV'2019)
- Vision Transformer (ICLR'2021)
- Swin Transformer (ICCV'2021)
- Twins (NeurIPS'2021)
- BEiT (ICLR'2022)
- ConvNeXt (CVPR'2022)
- MAE (CVPR'2022)
Supported methods:
- FCN (CVPR'2015/TPAMI'2017)
- ERFNet (T-ITS'2017)
- UNet (MICCAI'2016/Nat. Methods'2019)
- PSPNet (CVPR'2017)
- DeepLabV3 (ArXiv'2017)
- BiSeNetV1 (ECCV'2018)
- PSANet (ECCV'2018)
- DeepLabV3+ (CVPR'2018)
- UPerNet (ECCV'2018)
- ICNet (ECCV'2018)
- NonLocal Net (CVPR'2018)
- EncNet (CVPR'2018)
- Semantic FPN (CVPR'2019)
- DANet (CVPR'2019)
- APCNet (CVPR'2019)
- EMANet (ICCV'2019)
- CCNet (ICCV'2019)
- DMNet (ICCV'2019)
- ANN (ICCV'2019)
- GCNet (ICCVW'2019/TPAMI'2020)
- FastFCN (ArXiv'2019)
- Fast-SCNN (ArXiv'2019)
- ISANet (ArXiv'2019/IJCV'2021)
- OCRNet (ECCV'2020)
- DNLNet (ECCV'2020)
- PointRend (CVPR'2020)
- CGNet (TIP'2020)
- BiSeNetV2 (IJCV'2021)
- STDC (CVPR'2021)
- SETR (CVPR'2021)
- DPT (ArXiv'2021)
- Segmenter (ICCV'2021)
- SegFormer (NeurIPS'2021)
- K-Net (NeurIPS'2021)
Please refer to get_started.md for installation and dataset_prepare.md for dataset preparation.
Please see train.md and inference.md for the basic usage. There are also tutorials for customizing dataset, designing data pipeline, customizing modules, and customizing runtime. We also provide many training tricks for better training and useful tools for deployment.
A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.
Please refer to FAQ for frequently asked questions.
We appreciate all contributions. Please refer to CONTRIBUTING.md for the contributing guideline.
We thank openMMlab for the open-source libraries with excellent features.
- Dataset4EO: Datasets downloading and loading for Remote sensing community.
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}
}This project is released under the Apache 2.0 license.