在评估图像分类模型选择分数时,一个尺寸不适合所有人
Nermeen Abou Baker1, Uwe Handmann2
1Computer Science Department, Ruhr West University of Applied Sciences, Bottrop, Germany. nermeen.baker@hs-ruhrwest.de.
Scientific reports
|December 5, 2024
概括
本研究评估了14个可转移性得分,用于选择预训练的图像分类模型,从而降低计算成本. 调查结果显示,得分的有效性因数据集和模型类型而异,视觉转换器 (ViT) 经常表现出色.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 选择预训练模型用于图像分类通常需要广泛的微调.
- 缺乏对可转移性得分的标准化评估,这阻碍了有效的模型选择.
- 减少模型选择中的计算负担对于实际应用至关重要.
研究的目的:
- 评估14个可转移性得分的有效性,以对预训练模型进行排名.
- 提供一个一致的方法,在模型选择中平衡准确性和效率.
- 为了指导从业者在选择最佳模型时,不需要详尽的微调.
主要方法:
- 在11个基准数据集中评估了14个可转移性得分.
- 包括卷积神经网络 (CNN) 和视觉转换器 (ViT) 模型.
- 确保一致的实验条件,以减轻先前研究的变化.
主要成果:
- 根据数据集特征和模型架构,可转移性评分的有效性差异很大.
- 视觉变压器 (ViT) 模型显示出卓越的可转移性,特别是在细粒度数据集上.
- 没有一个单一的得分被证明是普遍最佳的;有效性取决于背景.
结论:
- 该研究提供了对选择适当的可转移性得分来进行优化模型选择的见解.
- 通过考虑计算效率,确定了适合资源有限环境的得分.
- 旨在促进在实践中更有效地部署图像分类模型.
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