废物分类中的基础模型的概括能力
Aloïs Babé1, Rémi Cuingnet2, Mihaela Scuturici3
1Université Lumière Lyon 2, CNRS, Ecole Centrale de Lyon, INSA Lyon, Université Claude Bernard, Lyon 1, LIRIS, UMR 5205, Bron 69676, France; Veolia Scientific & Technical Expertise Department, Maisons-Laffitte 78600, France.
Waste management (New York, N.Y.)
|March 7, 2025
概括
与标准模型相比,基金会模型显示工业废物分类的概括性优越. 较大的模型和预训练数据集提高了性能,参数高效微调 (PEFT) 证明了其有效性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 环境科学 环境科学
背景情况:
- 工业废物分类系统需要在不同的地点和时间段进行强有力的概括,以便在实践中部署.
- 基础模型为各种AI任务提供了适应性和强大的泛化潜力.
研究的目的:
- 调查工业废物分类基础模型的有效性.
- 评估它们在不同数据集和培训策略中的概括能力.
主要方法:
- 利用五个不同的废物分类数据集进行培训和交叉测试基础模型.
- 探索各种适应技术,包括标准微调和参数高效微调 (PEFT).
- 评估模型大小和预训练数据集大小对概括性能的影响.
主要成果:
- 基金会模型在废物分类的概括方面明显优于标准模型.
- 模型大小和预训练数据集规模与概括性能正相关.
- 参数高效微调 (PEFT) 证明了有效性,特别是在更大的基础模型中.
- 复杂的分类器头和简单的数据增强被发现是不必要的或无效的.
结论:
- 基础模型为开发通用工业废物分类系统提供了一个非常有前途的方法.
- 在这个领域,PEFT为大型基础模型提供了一个有效的适应策略.
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