The main priority this week has been communicating with other LexisNexis employees to fix the GNN Model 255 images constraint. As of right now, we are examining issues in the code itself rather than Azure/the cloud. While working on that, I have also been testing different models (besides the TensorFlow Transfer Learning one) to see what changes it will reflect on the accuracy percentages. Overall, the TensorFlow Models run smoothly on Jupyter Notebook with consistently high accuracy rates.
With the GNN HPCC Model image count limitation, I would only be able to train 255 out of close to 5,000 student/non-student images. Even with this limitation, I will still be able to bring it past the “proof of concept” stage by running the full dataset successfully on Jupyter Notebook using the Transfer Learning Model. However, ideally, the HPCC model will be processing the images on the cloud. Fixing the 255 image limitation will open the doors for more practical applications of the HPCC GNN model in the future even beyond the scope of my internship project, which makes it a top priority for this week.
