satellighte

📡 PyTorch Lightning Implementations of Recent Satellite Image Classification !

https://github.com/canturan10/satellighte

Science Score: 54.0%

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Links to: arxiv.org
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Low similarity (8.9%) to scientific vocabulary

Keywords

computer-vision deep-learning image-classification pytorch pytorch-lightning satellite state-of-the-art

Last synced: 6 months ago · JSON representation ·

Repository

📡 PyTorch Lightning Implementations of Recent Satellite Image Classification !

Basic Info

Host: GitHub
Owner: canturan10
License: mit
Language: Python
Default Branch: master
Homepage: https://canturan10.github.io/satellighte
Size: 1.39 MB

Statistics

Stars: 74
Watchers: 1
Forks: 7
Open Issues: 0
Releases: 4

Topics

computer-vision deep-learning image-classification pytorch pytorch-lightning satellite state-of-the-art

Created about 4 years ago · Last pushed over 2 years ago

Metadata Files

Readme License Citation

Satellite Image Classification

Website Docs Pypi

Demo Page

Satellighte

TABLE OF CONTENTS

About The Satellighte
Prerequisites
Installation
- From Pypi
- From Source
Usage Examples
APIs
Architectures
Datasets
Deployments
Training
Tests
Contributing
Contributors
Contact
License
References
Citations

About The Satellighte

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

Prerequisites

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 |

Installation

To install Satellighte, follow these steps:

From Pypi

bash pip install satellighte

From Source

bash git clone https://github.com/canturan10/satellighte.git cd satellighte pip install .

From Source For Development

bash git clone https://github.com/canturan10/satellighte.git cd satellighte pip install -e ".[all]"

Usage Examples

```python import imageio import satellighte as sat

img = imageio.imread("test.jpg")

model = sat.Classifier.frompretrained("modelconfig_dataset") model.eval()

results = model.predict(img)

[{'cls1': 0.55, 'cls2': 0.45}]

```

APIs

For more information, please refer to the APIs

Architectures

For more information, please refer to the Architectures

Datasets

[x] EuroSAT
[x] RESISC45

For more information, please refer to the Datasets

Deployments

For more information, please refer to the Deployment

Training

To training, follow these steps:

For installing Satellighte, please refer to the Installation.

bash python training/eurosat_training.py

For optional arguments,

bash python training/eurosat_training.py --help

Tests

During development, you might like to have tests run.

Install dependencies

bash pip install -e ".[test]"

Linting Tests

bash pytest satellighte --pylint --pylint-error-types=EF

Document Tests

bash pytest satellighte --doctest-modules

Coverage Tests

bash pytest --doctest-modules --cov satellighte --cov-report term

Contributing

To 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.

Contributors

Ouzcan Turan

You ?

Contact

If you want to contact me you can reach me at can.turan.10@gmail.com.

License

This project is licensed under MIT license. See LICENSE for more information.

References

The references used in the development of the project are as follows.

Citations

Click to expand!

```BibTeX @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} } ``` ```bibtex @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} } ``` ```bibtex @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} } ``` ```bibtex @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} } ``` ```bibtex @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} } ``` ```BibTeX @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} } ``` ```BibTeX @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} } ```

Give a if this project helped you!

This readme file is made using the readme-template

Owner

Name: Oğuzcan Turan
Login: canturan10
Kind: user
Company: turk.ai

Website: canturan10.github.io
Repositories: 2
Profile: https://github.com/canturan10

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this repository, please cite it as below."
preferred-citation:
  type: generic
  authors:
  - family-names: "Turan"
    given-names: "Oguzcan"
  title: "satellighte"
  url: "https://github.com/canturan10/satellighte"

GitHub Events

Total

Watch event: 3

Last Year

Watch event: 3

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 99
Total Committers: 3
Avg Commits per committer: 33.0
Development Distribution Score (DDS): 0.04

Top Committers

Name	Email	Commits
Oğuzcan Turan	c**0@g**m	95
Oğuzcan Turan	3**0@u**m	3
Oğuzcan Turan	o**n@t**m	1

Committer Domains (Top 20 + Academic)

turkai.com: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time

Total issues: 6
Total pull requests: 14
Average time to close issues: about 2 months
Average time to close pull requests: less than a minute
Total issue authors: 6
Total pull request authors: 1
Average comments per issue: 2.5
Average comments per pull request: 0.0
Merged pull requests: 14
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

sparkingdark (1)
kadirnar (1)
aniketmaurya (1)
princealy (1)
protestToViolence (1)
robmarkcole (1)

Pull Request Authors

canturan10 (13)

Top Labels

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documentation (1)

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Packages

Total packages: 1
Total downloads:
- pypi 85 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 16
Total maintainers: 1

pypi.org: satellighte

PyTorch Lightning Implementations of Recent Satellite Image Classification !

Homepage: https://github.com/canturan10/satellighte
Documentation: https://satellighte.readthedocs.io/
License: MIT License
Latest release: 0.2.5
published over 2 years ago

Versions: 16
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 85 Last month

Rankings

Stargazers count: 8.1%

Dependent packages count: 10.1%

Forks count: 14.2%

Average: 19.1%

Dependent repos count: 21.5%

Downloads: 41.4%

Maintainers (1)

canturan10

Last synced: 6 months ago

Dependencies

.github/workflows/release.yml actions

actions/checkout master composite
actions/setup-python v1 composite

deployment/fastapi/Dockerfile docker

python 3.8 build

deployment/fastapi/requirements.txt pypi

fastapi ==0.74.1
python-multipart ==0.0.5
uvicorn ==0.17.5

requirements.txt pypi

PyYAML *
fire *
imageio *
numpy *
pytorch_lightning *
rich *
scikit-learn *
setuptools ==67.8.0
torch *
torchmetrics *
torchvision *
uniplot *

setup.py pypi

satellighte

Science Score: 54.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Satellighte

Satellite Image Classification

About The Satellighte

Prerequisites

Installation

From Pypi

From Source

From Source For Development

Usage Examples

[{'cls1': 0.55, 'cls2': 0.45}]

APIs

Architectures

Datasets

Deployments

Training

Tests

Linting Tests

Document Tests

Coverage Tests

Contributing

Contributors

Ouzcan Turan

You ?

Contact

License

References

Citations

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: satellighte

Rankings

Maintainers (1)

Dependencies