The author argues that machine learning benchmarks have been successful in driving progress in the field, but their limitations and flaws have been overlooked, including the potential for gaming the metrics and overfitting to benchmark datasets. The book aims to shed light on why benchmarks work and what they are good for, covering the foundations of benchmarking, the ImageNet era, and recent ...