IML: Week 8 Style Transfer – Thomas Tai

 

Introduction

The goal for this week was to train the model using the given code. I ran the model training program using the given instructions. I had to repeat the download for the dataset a couple times since it failed. I found the qstat command to be very useful for checking if my training was still going on, since the training took a couple tries. Like others, I was unable to finish the training since the maximum runtime was 24 hours for the Intel AI Cloud. So, I modified the code to skip training and only include the checkpoint conversion to a format that ml5.js supports. Alternatively, you could modify the number of epochs to reduce training time. I was able to successfully get the model, which seems to only be a series of weights compiled by the program. I trained two models, but these are incomplete and likely need more training to have a better result.

Style Image:

shanghai

Input Images: 

Output Images:

I find this form of machine learning technology to be really cool, since it combines art and computer science together. This would not have been possible just a few years before. I have noticed that Google Photos sometimes gives me stylized suggestions for photos, and I would think that a variation of this model is used for their implementation of style transfer. I noticed artifacts and weird patterns in the output, so the model might require further training or modifications. Either way, I enjoyed the unique style it generated, and I look forward to seeing more machine generated art in the future.

Sources for Images:
Shanghai
New York City
Cute-Dog

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