Detection of supernovae for astronomers is a task where machine learning can help reduce time resources, while increasing accuracy. In this project we build a supernova classification system to allow detection of images from the Dark Energy Survey using a ConvNet model. Read more: https://medium.com/dessa-news/space-2-vec-fd900f5566
Check our the posts here: space2vec.com
The project behind the code is talked about in detail throughout the blog posts. But this is where the cool code stuff happens!
You can find the model in the /cnn-model directory where you can find all model code and links to data.
We have supplied requirements.txt file which you can use to setup the right environment. This was made for Python 3.6, so if you are getting errors about missing versions or something similar try removing anything after the "==" for that library in the requirements.txt and run again.
Maintained by Pippin Lee ([email protected]) and Cole Clifford ([email protected])
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