PlaNet: Classifying Planets with Neural Networks

After completing Jeremy Howard’s Deep Learning course, I wanted to put my skills to the test on something fun and interesting, so I set out to train a neural network that classified planets. I’m happy with the end result (and its cheeky name): plaNet.

I wanted to classify major solar system planets based on salient features. The issue with this approach is that there isn’t very much data to train a neural network on. I scraped AstroBin for amateur photos of planets, but I found that most of them simply looked like smudges, and the outer planets were either unrecognizable or missing entirely.

Some of the unaugmented training data used for Jupiter, mostly from NASA.

To get around these issues, I based my approach on two methods: data augmentation on my small dataset, and fine-tuning an existing neural network. Data augmentation is simple in Keras, so I dramatically increased my dataset size simply by applying transformations to my initial images. I fine-tuned my network on VGG’s ImageNet convolutional layers (a classic approach to transfer learning). I dropped out the last fully-connected layer, which was trained to classify everyday objects, and kept the convolutional layers. These layers are great for identifying features — edges, shapes, and patterns — that could still be found in my images of planets. At this point, I pre-calculated the output of the convolutional layer on the initial and augmented datasets in order to easily combine them into one feature set, then I was able to train with a relatively solid test accuracy (~90%). I used a high dropout rate in order to avoid overfitting to my small training dataset, and it seems to have worked.

I want to highlight the simplicity of this approach. Because we’re simply fine-tuning a pre-trained neural network, we can access what is essentially the state of the art in deep learning with just a few lines of code and a small amount of computing time and power (compared to training an entire network from scratch). My work was mostly in preparing the datasets and fine-tuning different parameters until I was happy with the results. If you haven’t already, I encourage you to take a look at the course online. Many thanks to Jeremy Howard for giving me a practical approach to something I’ve only had theoretical backing for so far.

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