- Confirm that the dataset size is 4,839 images
- Run the full dataset with different models in Jupyter Notebook
- MobileNetV2
- InceptionResNetV2
- NASNetMobile
- EfficientNet_V2
- bit
- Run GPU on the desktop
- On the HPCC GNN model, change the number of thor slaves (1, 2, 4, 8, 12, 20) and document how this variable affects the total cluster time (using MobileNetV2, 224x224x3 images, and 5 epochs)
- On the HPCC GNN model, change the CPU and memory. Run the various number of thor slaves again (1, 2, 4, 8, 12, 20) with 8 CPU and 16g memory)
- Write documentation for how to spray 4,000+ images into HPCC
- Write documentation for how to recreate this project using Jupyter Notebook and HPCC GNN
- Load this model onto my phone
- Write README.md files
- Create presentation with the following information
- What is a neural network
- What is CNN
- What is GNN
- What is image classification
- How did I get my data
- Results/how I trained my local device
- Results/how I used Jupyter Notebook (and link for others to recreate)
- Which model I used
- Move this job to HPCC (how we set it up) (tables with 1 thor, 2 thors, 4, 8, 12, and 20 in default)
- specify which model yielded what result
- Change to 8 CPU and 16g memory
- affect on time and accuracy
- Results from GPU
- GitHub link
- Jiras that I opened and which ones got resolved
- Future work
