For this week, I focused on data collection and annotations, as well as getting started on the prototype of our model.
I’ve set up our PyTorch environment and completed an initial YOLOv5 prototype. I’ve developed a basic pipeline, where images are pre-processed with OpenCV and then fed to the YOLOv5 model, and conducted some preliminary training and testing using small annotated datasets of grocery items. Furthermore, I have been sourcing and noting down relevant datasets on Kaggle/ Roboflow.
Currently, I am working on integrating the online datasets with the YOLOv5 model, and conducting some initial tests on accuracy as well as inference speed. I aim to test the inference speed locally on Raspberry Pi, as well as on cloud, to get a measure of latency of either set-up. I will also experiment with image processing methods using images taken from our fridge, in order to try and improve detection accuracy.
Looking to the future, I will have to obtain the necessary hardware(Raspberry Pi) in order to test the effectiveness of our model when run locally. I will also have to work on integrating the model with our peripheral device as well as the mobile application.