一个统一框架，让多模态自监督学习训练变得简单高效。
PrismSSL: One Interface, Many Modalities; A Single-Interface Library for Multimodal Self-Supervised Learning
- 用模块化代码统一音频、视觉、图数据和跨模态的自监督方法
- 支持一键启动训练、超参优化和分布式计算，提升实验效率
- 适合想快速实验多模态模型的研究者和开发者
我们提出 PrismSSL，一个 Python 库，将音频、视觉、图结构及跨模态场景下的先进自监督学习（SSL）方法统一到单一、可模块化的代码库中。该工具旨在帮助研究人员和实践者：(i) 仅需几行代码即可安装、配置并运行预训练任务；(ii) 复现紧凑的基准测试；(iii) 通过清晰的训练器与数据集抽象扩展新模态或方法。PrismSSL 已发布于 PyPI，采用 MIT 许可证，与 HuggingFace Transformers 深度集成，提供分布式训练、Optuna 超参搜索、LoRA 微调、嵌入可视化动画、Weights & Biases 日志记录以及彩色结构化终端输出等便捷功能。此外，还提供基于 Flask 的图形化仪表盘，支持低代码配置和启动训练流程。项目代码与数据配方将公开可复现。
原文摘要 · Abstract (English)
We present PrismSSL, a Python library that unifies state-of-the-art self-supervised learning (SSL) methods across audio, vision, graphs, and cross-modal settings in a single, modular codebase. The goal of the demo is to show how researchers and practitioners can: (i) install, configure, and run pretext training with a few lines of code; (ii) reproduce compact benchmarks; and (iii) extend the framework with new modalities or methods through clean trainer and dataset abstractions. PrismSSL is packaged on PyPI, released under the MIT license, integrates tightly with HuggingFace Transformers, and provides quality-of-life features such as distributed training in PyTorch, Optuna-based hyperparameter search, LoRA fine-tuning for Transformer backbones, animated embedding visualizations for sanity checks, Weights & Biases logging, and colorful, structured terminal logs for improved usability and clarity. In addition, PrismSSL offers a graphical dashboard - built with Flask and standard web technologies - that enables users to configure and launch training pipelines with minimal coding. The artifact (code and data recipes) will be publicly available and reproducible.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。