cl-metrics: A Stateless Python Library for Continual Learning Evaluation with SNN Energy-Aware Extensions

We present cl-metrics, a stateless, architecture-agnostic Python library that computes standard Continual Learning (CL) and Class-Incremental Learning (CIL) evaluation metrics from a raw per-task accuracy matrix — with no dependency on any training framework. The library fills a well-documented gap: dominant CIL frameworks (Avalanche, PyCIL, FACIL, Sequoia) embed metric computation inside their training loops, making it impossible to compute standard metrics from a pre-generated accuracy matrix without writing framework-specific wrapper code. cl-metrics is the scikit-learn.metrics equivalent for CIL evaluation. Metrics implemented (canonical formulations): Average Accuracy (AA), Backward Transfer (BWT),…