Project Type: Ingredient Recognition System
Hello! I am relatively new to working with datasets and image classification, so I would appreciate some advice. So, we were tasked with creating an Ingredient Recognition System using YOLOv8, where we need to classify around 50 ingredients. Since I am new to dataset preparation, I’m not sure how many images per class are actually needed to achieve good model performance.
We were also given a very short deadline of about one week, so we initially collected a large number of images to make our dataset more diverse. One of my teammates recommended around 500 images per classes, but I am quite unsure about the number of images. Is there any recommended approach for students in this situation? Should we reduce the number of images per class?
Any advice would be greatly appreciated. Thank you so much!