UNMAINTAINED. Deep Learning toolkit for Computer Vision.
|size (curr.)||3121 kB|
|license||BSD 3-Clause “New” or “Revised” License|
(Jan 2020) Luminoth is not maintained anymore. We recommend switching to Facebook’s Detectron2, which implements more modern algorithms supporting additional use cases.
Read the full documentation here.
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 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.
Just install from PyPI:
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.
If you wish to train using Google Cloud ML Engine, the optional dependencies must be installed:
pip install luminoth[gcloud]
Installing from source
First, clone the repo on your machine and then install with
git clone https://github.com/tryolabs/luminoth.git cd luminoth pip install -e .
Check that the installation worked
Currently, we support the following models:
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.
See Training your own model to learn how to train locally or in Google Cloud.
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:
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.