A retailer uses overhead photos of product tops on shelves to detect which items were removed. They labeled 1,000 images covering 10 different items, but model performance is poor. Which approach best meets the company's long-term needs?

The best approach is to augment the training set by generating image variants. Data augmentation artificially expands the dataset by creating modified versions of existing images (e.g., rotations, flips, translations). This increases the diversity of the training data, making the model more robust and less prone to overfitting, which is crucial when the initial dataset (1,000 images for 10 items) is relatively small. Converting images to grayscale removes valuable color information, which could be important for distinguishing products, and would likely worsen performance. Reducing the number of item classes from 10 to 2 doesn't address the underlying data scarcity problem for the full set of items and only delays the challenge. Placing colored stickers on each item is a significant operational change and doesn't leverage the existing image data effectively.