This week, I worked on testing the classifier we had on a much larger dataset in order to ensure our algorithm generalized well to several types of bananas. Our algorithm achieved a 2.14 % misclassification rate for good bananas out of a dataset comprising approximately 2000 images of good bananas. As for bad bananas, our algorithm achieved a 0.94 % misclassification rate out of a dataset comprising approximately 2800 images of bad bananas. The algorithm seemed to be running quite efficiently as well, so it was good to see that we seemed to be meeting the targets we had set by a huge margin.
Besides this, I worked on clicking images of bananas for checking if our algorithm worked well even on images that we had clicked, and it achieved quite an amazing classification rate and performance for images that we clicked. I also started working on developing the AlexNet classification algorithm code to see if that could give us an improved classification result over our existing code.
We seem to be on track for now, but the upcoming week is going to be a hectic one. We planned it accordingly since we have a bit of a break, but I hope we can stay on track with our progress. For future steps for my part, I need to test our model on more images clicked personally, complete the AlexNet classification, and start working on code for object classification so that by the time we transition to testing for apples and oranges, I have the code in place to be able to check if a fruit is an apple, an orange, or a banana.