对比三种视觉模型方法，发现迁移学习效果最好，自定义模型更省资源。
A Comparative Study of Custom CNNs, Pre-trained Models, and Transfer Learning Across Multiple Visual Datasets
- 在五个数据集上比较自建CNN、预训练模型和迁移学习
- 迁移学习准确率最高，自建模型训练更快且参数少
- 适合算力有限或追求效率的实践者参考
卷积神经网络（CNN）因其从原始像素中学习分层特征的能力，已成为视觉识别的标准方法。实践中，研究者常在三类方案间选择：(i) 从零开始训练小型自定义CNN；(ii) 将大型预训练CNN作为固定特征提取器；(iii) 通过部分或全微调预训练主干网络进行迁移学习。本报告在五个真实世界图像分类数据集上对这三种范式进行了受控比较，涵盖路面缺陷识别、农业品种鉴定、果实/叶片病害识别、行人过道侵入检测及非法车辆识别。模型以准确率和宏平均F1分数评估，并辅以每轮训练时间与参数量等效率指标。结果表明，迁移学习始终表现最优；而自定义模型在计算与内存受限时展现出理想的精度-效率平衡。
原文摘要 · Abstract (English)
Convolutional Neural Networks (CNNs) are a standard approach for visual recognition due to their capacity to learn hierarchical representations from raw pixels. In practice, practitioners often choose among (i) training a compact custom CNN from scratch, (ii) using a large pre-trained CNN as a fixed feature extractor, and (iii) performing transfer learning via partial or full fine-tuning of a pre-trained backbone. This report presents a controlled comparison of these three paradigms across five real-world image classification datasets spanning road-surface defect recognition, agricultural variety identification, fruit/leaf disease recognition, pedestrian walkway encroachment recognition, and unauthorized vehicle recognition. Models are evaluated using accuracy and macro F1-score, complemented by efficiency metrics including training time per epoch and parameter counts. The results show that transfer learning consistently yields the strongest predictive performance, while the custom CNN provides an attractive efficiency--accuracy trade-off, especially when compute and memory budgets are constrained.
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。