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        "description": "# Omnis: Deep Learning for Everyone\n\n[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/omnis-labs-company/omnis/blob/master/LICENSE)\n\n## You have just found Omnis.\n\nOmnis is a library of deep neural network applications, written in Python and capable of running on top of [Keras](https://github.com/keras-team/keras) and [Tensorflow](https://www.tensorflow.org/). It was developed with a focus on enabling fast application of deep learning.\n\nUse Omnis if you need a deep learning library that:\n\n- Is EASY to learn.\n- Allows for easy and fast use.\n- Supports CNN, LSTM, GAN applications.\n\nOmnis is compatible with: __Python 3.6-3.7__.\n\n------------------\n\n\n## Getting started: Implement a deep learning application with 4 lines of code!\n\nThe core data structure of Omnis is Application which is designed to be easy to use in each field.\n\nHere is an `Image Classification` example with the [`Caltech 101`](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) dataset:\n\n```python\nfrom omnis.application.image_processing.image_classification.image_classification import ImageClassification\n```\n\nChoose an application:\n\n```python\nimage_classifier = ImageClassification()\n```\n\nTrain:\n\n```python\nimage_classifier.train(data_path='101_ObjectCategories', epochs=5, batch_size=32, model_type='densent121')\n```\n\nNow you can use the application to classify images:\n\n```python\nprediction_result = image_classifier.predict(data_path = '101_ObjectCategories/accordion')\n\nprint(prediction_result)\n```\n\nSave / Load:\n\n```python\nimage_classifier.save(model_path=\"weights.h5\")\n```\n```python\nimage_classifier = ImageClassification(model_path=\"weights.h5\")\n```\n\nFor a more in-depth tutorial about Omnis, you can check out:\n\nIn the [examples folder](https://github.com/omnis-labs-company/omnis/tree/master/example) of the repository, you will find more applications.\n\n------------------\n\n\n## Installation\n\nBefore installing Omnis, please prepare NVIDIA GPU(s) and install TensorFlow GPU and Keras using conda.\n\nThen, you can install Omnis itself. There are two ways to install Omnis:\n\n- **Install Omnis from PyPI (recommended):**\n\nIf you don't use a conda virtual environment, you can run the command below (not recommended):\n\n```sh\nsudo pip install omnis\n```\n\nIf you are using a conda virtual environment, you may want to avoid using sudo:\n\n```sh\npip install omnis\n```\n\n- **Alternatively: install Omnis from the GitHub source:**\n\nFirst, clone Omnis using `git`:\n\n```sh\ngit clone https://github.com/omnis-labs-company/omnis.git\n```\n\n Then, `cd` to the Omnis folder and run the install command:\n```sh\ncd omnis\npython setup.py install\n```\n\n------------------\n\n\n## Guiding principles\n\n- __Simplicity.__ Omnis pursues a simple architecture. Designing a software with simple architecture not only helps you to understand the code easily but also helps your painful debugging.\n\n- __Easiness.__ Don't worry about complicated algorithms or theories or mathematics. Omnis will handle difficult stuffs for you. Just learn how to use deep neural networks and USE THIS!\n\n- __Modularity.__ No spaghetti code!\n\n------------------\n\n\n## Support\n\nYou can ask questions and join the development \n\n- On the [Omnis Slack channel](https://omnisteamworkspace.slack.com).\n\nYou can also post **bug reports and feature requests** (only) in [GitHub issues](https://github.com/omnis-labs-company/omnis/issues). Make sure to read [our guidelines](https://github.com/omnis-labs-company/omnis/blob/master/CONTRIBUTING.md) first.\n\n------------------\n\n\n## Why this name, Omnis?\n\nOmnis means _EVERY_ in Latin. The goal of Omnis is to make deep learning technologies easier so that _EVERY_ one can use deep learning technologies without headache.\n\n------------------",
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