Traditional machine learning systems are typically designed for static data distributions, which suffer from catastrophic forgetting when learning from evolving data streams. Class-Incremental Learning (CIL) addresses this challenge by enabling learning systems to continuously learn new classes while preserving prior knowledge. With the rise of pre-trained models (PTMs) such as CLIP, leveraging their strong generalization and semantic alignment capabilities has become a promising direction in CIL. However, existing CLIP-based CIL methods are often scattered across disparate codebases, rely on inconsistent configurations, hindering fair comparisons, reproducibility, and practical adoption. Therefore, we propose C3Box (CLIP-based Class-inCremental learning toolBOX), a modular and comprehensive Python toolbox. C3Box integrates representative traditional CIL methods, ViT-based CIL methods, and state-of-the-art CLIP-based CIL methods into a unified CLIP-based framework. By inheriting the streamlined design of PyCIL, C3Box provides a JSON-based configuration and standardized execution pipeline. This design enables reproducible experimentation with low engineering overhead and makes C3Box a reliable benchmark platform for continual learning research. Designed to be user-friendly, C3Box relies only on widely used open-source libraries and supports major operating systems. The code is available at https://github.com/LAMDA-CL/C3Box.

翻译：传统机器学习系统通常针对静态数据分布设计，在面对动态演化的数据流时易受灾难性遗忘的影响。类增量学习通过使学习系统能够持续学习新类别并保持已有知识，从而应对这一挑战。随着CLIP等预训练模型的兴起，利用其强大的泛化能力与语义对齐特性已成为类增量学习领域的重要研究方向。然而，现有的基于CLIP的类增量学习方法通常分散于不同的代码库中，且依赖不一致的配置，这阻碍了公平比较、可复现性及实际应用。为此，我们提出C3Box（基于CLIP的类增量学习工具箱），这是一个模块化、功能全面的Python工具箱。C3Box将代表性的传统类增量学习方法、基于ViT的类增量学习方法以及最先进的基于CLIP的类增量学习方法整合到统一的基于CLIP的框架中。通过继承PyCIL的简洁设计，C3Box提供基于JSON的配置与标准化的执行流程。该设计使得实验可复现且工程开销低，使C3Box成为持续学习研究中可靠的基准平台。C3Box注重用户友好性，仅依赖广泛使用的开源库并支持主流操作系统。代码已发布于https://github.com/LAMDA-CL/C3Box。