Quantitative Evaluation Metrics for Image Captioning

Measuring how well an AI model describes an image requires more than just subjective human judgment; it demands precise, quantitative metrics. This text-based course guides you through the foundational and modern mathematical metrics used to evaluate image captioning models, helping you confidently assess and compare model performance. What you'll learn: - Understand the core concepts of natural language generation evaluation and basic terminology - Compute standard n-gram overlap metrics including BLEU, ROUGE, and METEOR - Analyze specialized captioning metrics like CIDEr and SPICE to measure consensus and semantic structure - Explore modern evaluation paradigms, including embedding-based and vision-language alignment metrics like CLIPScore - Practice interpreting quantitative scores to diagnose model weaknesses and guide training adjustments You will start with key terminology and basic concepts before exploring traditional, specialized, and modern evaluation workflows through detailed written explanations. This course is designed for beginners in computer vision and natural language processing, with no advanced prerequisites required. Start reading to master the quantitative standards of image caption generation.