Following Lili and Roger’s advice, I changed the code from UNSIGNED1 (which is 1 byte) to UNSIGNED4 (4 bytes). Previously, the maximum was 255 images before the model wouldn’t run anymore. Today, I tested 256 images and it successfully worked.
The next step was to spray all 4,000+ images and run the model with the complete dataset.
With 4,839 images and 4 thor slaves, the model took 1 hour and 13 minutes to run 20 epochs, reaching 100% accuracy on the student images after 2 epochs. When I tested 5 epochs, the training used 31 minutes and 22 seconds. Next week, I will keep retraining the model to see if/how much the speed increases when continuously processing the same images.
This was a huge step for this internship project and the HPCC GNN model as it can now process exponentially more images for multiple purposes.
