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.
Luminoth is a computer vision toolkit made with Tensorflow and Sonnet. Our main objective is to create tools and code to easily train and use deep learning models for computer vision problems.
- Code that is both easy to understand and easy to extend.
- Out-of-the-box state of the art models.
- Straightforward implementations with TensorBoard support.
- Cloud integration for training and deploying.
DISCLAIMER: This is currently a pre-pre-alpha release, we decided to open-source it up for those inquisive minds that don't mind getting their hands dirty with rough edges of code.
We started building Luminoth at Tryolabs after realizing we always ended up rewriting many of the common Tensorflow boilerplate code and models over and over. Instead of just building a cookie-cutter for Tensorflow we started to think about what other features we could benefit from, and how an ideal toolkit would look like.
It is indisputable that TensorFlow is currently the most mature Deep Learning framework, and even though we love (truly love) other frameworks as well, especially PyTorch, our customers demand stable and production ready Machine Learning solutions.
Sonnet fits perfectly with our mission to build code that is easy to follow and to extend. It is tricky to build a computation graph that is abstract and low-level at the same time to allows us to build complex models, and luckily Sonnet is a library that provides just that.
Luminoth currently supports Python 2.7 and 3.4–3.6.
If TensorFlow and Sonnet are already installed, Luminoth will use those versions.
You can check the installation is working by running lumi --help
.
Just run:
$ pip install luminoth
This will install the CPU versions of TensorFlow & Sonnet if you don't have them.
- Install TensorFlow with GPU support.
- Install Sonnet with GPU support:
$ pip install dm-sonnet-gpu
- Install Luminoth from PyPI:
$ pip install luminoth
First, clone the repo on your machine and then install with pip
:
$ git clone https://github.com/tryolabs/luminoth.git
$ cd luminoth
$ pip install -e .
Currently we are focusing on object detection problems, and have a fully functional version of Faster RCNN. There are more models in progress (SSD and Mask RCNN to name a couple), and we look forward to opening up those implementations.
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.
Convert datasets to TensorFlow's .tfrecords
for efficient processing using the computation graphs (and for cloud support).
lumi dataset voc --data-dir ~/dataset/voc/ --output-dir ~/dataset/voc/tf/
lumi dataset imagenet --data-dir ~/dataset/imagenet/ --output-dir ~/dataset/imagenet/tf/
lumi dataset coco --data-dir ~/dataset/coco/ --output-dir ~/dataset/coco/tf/
Check our TRAINING.md on 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 whats 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 /tmp/luminoth
. You can use TensorBoard by running:
tensorboard --logdir /tmp/luminoth