Computer vision toolkit based on TensorFlow
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Luminoth 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).
Read the full documentation [here](http://luminoth.readthedocs.io/).
![Example of Object Detection with Faster R-CNN](https://user-images.githubusercontent.com/1590959/36434494-e509be42-163d-11e8-99c1-d1aa728929ec.jpg)
> 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.
Luminoth currently supports Python 2.7 and 3.4–3.6.
To 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.
## Installing Luminoth
Just install from PyPI:
`bash pip install luminoth `
Optionally, 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.
### Google Cloud
If you wish to train using Google Cloud ML Engine, the optional dependencies must be installed:
`bash pip install luminoth[gcloud] `
## Installing from source
First, clone the repo on your machine and then install with
`bash git clone https://github.com/tryolabs/luminoth.git cd luminoth pip install -e . `
## Check that the installation worked
# Supported models
Currently, we support the following models:
- Object Detection * [Faster R-CNN](https://arxiv.org/abs/1506.01497) * [SSD](https://arxiv.org/abs/1512.02325)
There 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:
lumi --help or
lumi <subcommand> --help
and a list of available options with descriptions will show up.
## Working with datasets
See [Adapting a dataset](http://luminoth.readthedocs.io/en/latest/usage/dataset.html).
See [Training your own model](http://luminoth.readthedocs.io/en/latest/usage/training.html) to learn how to train locally or in Google Cloud.
## Visualizing results
We 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.
By default summary and graph logs are saved to
jobs/ under the current directory. You can use TensorBoard by running:
`bash tensorboard --logdir path/to/jobs `
## Why the name?
> 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. > > – [Dark Visor - Wikitroid](http://metroid.wikia.com/wiki/Dark_Visor) >
Copyright © 2018, [Tryolabs](https://tryolabs.com). Released under the [BSD 3-Clause](LICENSE).
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
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