TABLE OF CONTENTS
Satellighte is an image classification library that consist state-of-the-art deep learning methods. It is a combination of the words 'Satellite' and 'Light', and its purpose is to establish a light structure to classify satellite images, but to obtain robust results.
Satellite image classification is the most significant technique used in remote sensing for the computerized study and pattern recognition of satellite information, which is based on diversity structures of the image that involve rigorous validation of the training samples depending on the used classification algorithm.
Source: paperswithcode
Before you begin, ensure you have met the following requirements:
| requirement | version |
|---|---|
| imageio | ~=2.15.0 |
| numpy | ~=1.22.0 |
| pytorch_lightning | ~=1.7.0 |
| scikit-learn | ~=1.0.2 |
| torch | ~=1.9.1 |
To install Satellighte, follow these steps:
pip install satellightegit clone https://github.com/canturan10/satellighte.git
cd satellighte
pip install .git clone https://github.com/canturan10/satellighte.git
cd satellighte
pip install -e ".[all]"import imageio
import satellighte as sat
img = imageio.imread("test.jpg")
model = sat.Classifier.from_pretrained("model_config_dataset")
model.eval()
results = model.predict(img)
# [{'cls1': 0.55, 'cls2': 0.45}]- Available Models
- Available Versions for a Spesific Model
- Latest Version for a Spesific Model
- Pretrained Model
- Model with Random Weight Initialization
- Pretrained Arch Model
- Arch Model with Random Weight Initialization
For more information, please refer to the APIs
For more information, please refer to the Architectures
For more information, please refer to the Datasets
For more information, please refer to the Deployment
To training, follow these steps:
For installing Satellighte, please refer to the Installation.
python training/eurosat_training.pyFor optional arguments,
python training/eurosat_training.py --helpDuring development, you might like to have tests run.
Install dependencies
pip install -e ".[test]"pytest satellighte --pylint --pylint-error-types=EFpytest satellighte --doctest-modulespytest --doctest-modules --cov satellighte --cov-report termTo contribute to Satellighte, follow these steps:
- Fork this repository.
- Create a branch:
git checkout -b <branch_name>. - Make your changes and commit them:
git commit -m '<commit_message>' - Push to the original branch:
git push origin - Create the pull request.
Alternatively see the GitHub documentation on creating a pull request.
 
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If you want to contact me you can reach me at can.turan.10@gmail.com.
This project is licensed under MIT license. See LICENSE for more information.
The references used in the development of the project are as follows.
Click to expand!
@misc{dai2021coatnet,
title={CoAtNet: Marrying Convolution and Attention for All Data Sizes},
author={Zihang Dai and Hanxiao Liu and Quoc V. Le and Mingxing Tan},
year={2021},
eprint={2106.04803},
archivePrefix={arXiv},
primaryClass={cs.CV}
}@article{DBLP:journals/corr/ChengHL17,
author = {Gong Cheng and
Junwei Han and
Xiaoqiang Lu},
title = {Remote Sensing Image Scene Classification: Benchmark and State of
the Art},
journal = {CoRR},
volume = {abs/1703.00121},
year = {2017},
url = {http://arxiv.org/abs/1703.00121},
eprinttype = {arXiv},
eprint = {1703.00121},
timestamp = {Mon, 02 Dec 2019 09:32:19 +0100},
biburl = {https://dblp.org/rec/journals/corr/ChengHL17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}@article{helber2019eurosat,
title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2019},
publisher={IEEE}
}@inproceedings{helber2018introducing,
title={Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
pages={204--207},
year={2018},
organization={IEEE}
}@article{DBLP:journals/corr/abs-1801-04381,
author = {Mark Sandler and
Andrew G. Howard and
Menglong Zhu and
Andrey Zhmoginov and
Liang{-}Chieh Chen},
title = {Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification,
Detection and Segmentation},
journal = {CoRR},
volume = {abs/1801.04381},
year = {2018},
url = {http://arxiv.org/abs/1801.04381},
archivePrefix = {arXiv},
eprint = {1801.04381},
timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1801-04381.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}@article{DBLP:journals/corr/abs-1905-11946,
author = {Mingxing Tan and
Quoc V. Le},
title = {EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
journal = {CoRR},
volume = {abs/1905.11946},
year = {2019},
url = {http://arxiv.org/abs/1905.11946},
eprinttype = {arXiv},
eprint = {1905.11946},
timestamp = {Mon, 03 Jun 2019 13:42:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-11946.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}@article{DBLP:journals/corr/HeZRS15,
author = {Kaiming He and
Xiangyu Zhang and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {CoRR},
volume = {abs/1512.03385},
year = {2015},
url = {http://arxiv.org/abs/1512.03385},
eprinttype = {arXiv},
eprint = {1512.03385},
timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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