One in five pictures I take while hiking are pictures of the trails themselves. Previously, I experimented with using machine learning to automatically tag those pictures. After reading about how machine learning can be used not just for image classification, but also for generating images, I was curious to see what would happen if I tried this approach on my pictures.

Here are a few randomly selected pictures of trails, out of the more than 5,000 pictures of trails I have taken over the past 10 years:

actual trails

After a bit of number crunching with a Deep Convolutional Generative Adversarial Network (where part of the network is trained to fake images, while the other part is simultaneously being trained to detect fake images), here are a few randomly generated trail pictures:

fake trails

There is some blurriness and some distortions, but overall the results are pretty convincing! There are forest trails, trails through alpine meadows, rocky trails, and even a few snow-covered trails. But none of these trails exist in the real world.


  • I ended up using PyTorch, which happened to have suitable sample code. Keras (my go-to framework) turned out to be a bit too high-level to handle this kind of problem efficiently, and Tensorflow, being more low-level, would have required more code.

  • Unfortunately, scaling up beyond 64x64 thumbnails to 128x128 or larger turns out to not be a simple matter of throwing more computing resources at the problem.

  • The network was trained for 275 epochs (iterations through the entire data set). This animation shows how the generated images improved over time:

  • The training was done on a fast machine with a GPU (obtained from Amazon SageMaker for a total cost of about $2); running the same code on my laptop would have taken days, rather than just under an hour.

  • Found this list of tips that might further improve the results.