Package: satellighte
SatellighteSatellite Image ClassificationWebsite•Docs•PypiDemo PageTABLE OF CONTENTSAbout The SatellightePrerequisitesInstallationFrom PypiFrom SourceUsage ExamplesAPIsArchitecturesDatasetsDeploymentsTrainingTestsContributingContributorsContactLicenseReferencesCitationsAbout The SatellighteSatellighteis 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 classificationis 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:paperswithcodePrerequisitesBefore you begin, ensure you have met the following requirements:requirementversionimageio~=2.15.0numpy~=1.22.0pytorch_lightning~=1.7.0scikit-learn~=1.0.2torch~=1.9.1InstallationTo install Satellighte, follow these steps:From PypipipinstallsatellighteFrom Sourcegitclonehttps://github.com/canturan10/satellighte.gitcdsatellighte
pipinstall.From Source For Developmentgitclonehttps://github.com/canturan10/satellighte.gitcdsatellighte
pipinstall-e".[all]"Usage Examplesimportimageioimportsatellighteassatimg=imageio.imread("test.jpg")model=sat.Classifier.from_pretrained("model_config_dataset")model.eval()results=model.predict(img)# [{'cls1': 0.55, 'cls2': 0.45}]APIsAvailable ModelsAvailable Versions for a Spesific ModelLatest Version for a Spesific ModelPretrained ModelModel with Random Weight InitializationPretrained Arch ModelArch Model with Random Weight InitializationFor more information, please refer to theAPIsArchitecturesCoAtNetEfficientNetMobileNetV2ResNetFor more information, please refer to theArchitecturesDatasetsEuroSATRESISC45For more information, please refer to theDatasetsDeploymentsFastAPIONNXDeepSparseTensorFlowTensorFlow LiteFor more information, please refer to theDeploymentTrainingTo training, follow these steps:For installing Satellighte, please refer to theInstallation.pythontraining/eurosat_training.pyFor optional arguments,pythontraining/eurosat_training.py--helpTestsDuring development, you might like to have tests run.Install dependenciespipinstall-e".[test]"Linting Testspytestsatellighte--pylint--pylint-error-types=EFDocument Testspytestsatellighte--doctest-modulesCoverage Testspytest--doctest-modules--covsatellighte--cov-reporttermContributingTo contribute toSatellighte, 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 originCreate the pull request.Alternatively see theGitHubdocumentation oncreating a pull request.ContributorsOğuzcan TuranYou ?ContactIf you want to contact me you can reach me atcan.turan.10@gmail.com.LicenseThis project is licensed underMITlicense. SeeLICENSEfor more information.ReferencesThe references used in the development of the project are as follows.Img ShieldsGitHub PagesFastFacePyTorch LightningTorchvisionCitationsClick 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 andJunwei Han andXiaoqiang Lu},title={Remote Sensing Image Scene Classification: Benchmark and State ofthe 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 andAndrew G. Howard andMenglong Zhu andAndrey Zhmoginov andLiang{-}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 andQuoc 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 andXiangyu Zhang andShaoqing Ren andJian 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}}Give a ⭐️ if this project helped you!This readme file is made using thereadme-template