Evaluating image segmentation models for background removal for Images

This post details the evaluation and selection process for image segmentation models to implement a background removal feature within Cloudflare Images. It introduces image segmentation concepts, explains accuracy metrics (Intersection over Union, Dice coefficient, pixel accuracy), and compares the performance of U2-Net, IS-Net, BiRefNet, and SAM models based on these metrics. The technical contribution lies in the methodology for evaluating and selecting AI models for a specific product capability, highlighting the trade-offs between different model architectures and their suitability for real-world applications.