This week, we finalized the idea for our project, and successfully ironed out some issues.
Notably, I am in charge of the CV system; we were able to find a dataset of many fruits and vegetables (Fruits360 Dataset) which we could possibly use to train our CNN classifier. It is a fairly extensive dataset, with 90483 images and 131 classes of fruits and vegetables. Following a TA’s suggestion, we were also be able to find a ResNet based CNN classifier which we could potentially use for our project, and which I am currently trying to implement.
However, Prof Mario’s observed that the dataset images consisted of a white background, which implied that if trained using these images, our classifier might not be able to detect fruits in an arbitrary background. With this in mind, I came up with the idea of using a platform and a white screen that fruit can be put on, thus allowing us to not only easily detect and segment out the fruit from the (white) background, but also allow us to use the extensive dataset.
I am also fairly proud of my contribution to the “Use Case Requirements” part of the proposal presentation, where we considered our product’s speed accuracy and cost metrics from the perspective of tangible monetary cost.