Our group finally settled on using a Raspberry Pi rather than an FPGA or a Jetson as our hardware. This has meant I no longer need to research neural network implementation on FPGA, but we did receive useful advice in our weekly meeting as a result of our team’s research into this topic. However, this has given me more time to research the exact nature of our neural network. We are definitely going to be moving forward with a CNN. As for what extra layers we will be using on top of our architecture, this is still being determined and will most likely have to be fine tuned over the course of the semester. However, it currently looks like our transfer learning model will implement an initial convolutional layer, followed by 5 to 6 feature layers that will be trained via the user uploaded images of their pet(s).
As for schedule, I am back on track with where I wanted to be. Primary research is completed and implementation is starting. This week I want to start to get a functional transfer learning model as a starting point for testing and training.
Neural networks were covered in 15-301, Introduction to Machine Learning, which I took last year. I have done extra study on various forums looking at examples using the Inception architecture to become more familiar with this particular piece of additional software, but I am already comfortable with the area due to Intro to ML and Computational Neuroscience, which also covered neural networks.