Splits and new classified compositions!

Posted: September 20, 2019 at 7:14 pm

One thing I realized in my previous experiments was that I did not change the train/validate/test split. So I ran a few experiments with different splits, 50/25/25 was my initial choice. I tried 80/10/10, 75/15/15 and 60/20/20. My results showed that 75/15/15 seemed to work the best and I wrote some code to classify new images using that trained model. The following are the results! I think the classification is actually working quite well; a couple compositions I consider “bad” made it in there, but looking at these two sets I’m quite happy with the results.

“Good” Compositions
“Bad” Compositions

My next ML steps are:

  • finalize my architecture and train the final model
  • integrate the painting generator and face detection to run as a prototype that logs looking durations for each composition
  • run some experiments using this new dataset collected in the ‘wild’ and decide on thresholds for mapping from duration of looking to “good” and “bad” labels.
  • finally determine the best approach to running training code on the Jetson (embed keras? use ANNetGPGPU? FANN?) and implement it.