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            "License :: OSI Approved :: BSD License",
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            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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            "Topic :: Scientific/Engineering :: Mathematics",
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        "description": "[![Luminoth](https://user-images.githubusercontent.com/270983/31414425-c12314d2-ae15-11e7-8cc9-42d330b03310.png)](https://luminoth.ai)\n\n---\n\n[![Build Status](https://travis-ci.org/tryolabs/luminoth.svg?branch=master)](https://travis-ci.org/tryolabs/luminoth)\n[![Documentation Status](https://readthedocs.org/projects/luminoth/badge/?version=latest)](http://luminoth.readthedocs.io/en/latest/?badge=latest)\n[![codecov](https://codecov.io/gh/tryolabs/luminoth/branch/master/graph/badge.svg)](https://codecov.io/gh/tryolabs/luminoth)\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\n\nLuminoth is an open source toolkit for **computer vision**. Currently, we support object detection, but we are aiming for much more. It is built in Python, using [TensorFlow](https://www.tensorflow.org/) and [Sonnet](https://github.com/deepmind/sonnet).\n\nRead the full documentation [here](http://luminoth.readthedocs.io/).\n\n![Example of Object Detection with Faster R-CNN](https://user-images.githubusercontent.com/1590959/36434494-e509be42-163d-11e8-99c1-d1aa728929ec.jpg)\n\n> **DISCLAIMER**: Luminoth is still alpha-quality release, which means the internal and external interfaces (such as command line) are very likely to change as the codebase matures.\n\n# Installation\n\nLuminoth currently supports Python 2.7 and 3.4\u20133.6.\n\n## Pre-requisites\n\nTo use Luminoth, [TensorFlow](https://www.tensorflow.org/install/) must be installed beforehand. If you want **GPU support**, you should install the GPU version of TensorFlow with `pip install tensorflow-gpu`, or else you can use the CPU version using `pip install tensorflow`.\n\n## Installing Luminoth\n\nJust install from PyPI:\n\n```bash\npip install luminoth\n```\n\nOptionally, Luminoth can also install TensorFlow for you if you install it with `pip install luminoth[tf]` or `pip install luminoth[tf-gpu]`, depending on the version of TensorFlow you wish to use.\n\n### Google Cloud\n\nIf you wish to train using **Google Cloud ML Engine**, the optional dependencies must be installed:\n\n```bash\npip install luminoth[gcloud]\n```\n\n## Installing from source\n\nFirst, clone the repo on your machine and then install with `pip`:\n\n```bash\ngit clone https://github.com/tryolabs/luminoth.git\ncd luminoth\npip install -e .\n```\n\n## Check that the installation worked\n\nSimply run `lumi --help`.\n\n# Supported models\n\nCurrently, we support the following models:\n\n* **Object Detection**\n  * [Faster R-CNN](https://arxiv.org/abs/1506.01497)\n  * [SSD](https://arxiv.org/abs/1512.02325)\n\nWe are planning on adding support for more models in the near future, such as [RetinaNet](https://arxiv.org/abs/1708.02002) and [Mask R-CNN](https://arxiv.org/abs/1703.06870).\n\nWe also provide **pre-trained checkpoints** for the above models trained on popular datasets such as [COCO](http://cocodataset.org/) and [Pascal](http://host.robots.ox.ac.uk/pascal/VOC/).\n\n# Usage\n\nThere is one main command line interface which you can use with the `lumi` command. Whenever you are confused on how you are supposed to do something just type:\n\n`lumi --help` or `lumi <subcommand> --help`\n\nand a list of available options with descriptions will show up.\n\n## Working with datasets\n\nSee [Adapting a dataset](http://luminoth.readthedocs.io/en/latest/usage/dataset.html).\n\n## Training\n\nSee [Training your own model](http://luminoth.readthedocs.io/en/latest/usage/training.html) to learn how to train locally or in Google Cloud.\n\n## Visualizing results\n\nWe strive to get useful and understandable summary and graph visualizations. We consider them to be essential not only for monitoring (duh!), but for getting a broader understanding of what's going under the hood. The same way it is important for code to be understandable and easy to follow, the computation graph should be as well.\n\nBy default summary and graph logs are saved to `jobs/` under the current directory. You can use TensorBoard by running:\n\n```bash\ntensorboard --logdir path/to/jobs\n```\n\n## Why the name?\n\n> The Dark Visor is a Visor upgrade in Metroid Prime 2: Echoes. Designed by the **Luminoth** during the war, it was used by the Champion of Aether, A-Kul, to penetrate Dark Aether's haze in battle against the Ing.\n>\n> -- [Dark Visor - Wikitroid](http://metroid.wikia.com/wiki/Dark_Visor)\n>\n\n# License\n\nCopyright \u00a9 2018, [Tryolabs](https://tryolabs.com).\nReleased under the [BSD 3-Clause](LICENSE).\n\n\n",
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