对比九种CNN模型在马与摩托车分类中的表现，发现数据增强显著提升小类检测效果。
Comparative Study of CNN Architectures for Binary Classification of Horses and Motorcycles in the VOC 2008 Dataset
- 对比九种CNN架构，用数据增强缓解类别不平衡问题。
- ConvNeXt-Tiny在马和摩托车检测上分别达95.53%和89.12%平均精度。
- 深层网络更受益于数据增强，为小样本分类提供选型参考。
本文对九种卷积神经网络架构在VOC 2008数据集上进行二分类任务的全面评估，针对显著的类别不平衡问题，采用少数类数据增强技术。实验比较了ResNet-50、ConvNeXt-Tiny、DenseNet-121及Vision Transformer等现代架构在多个性能指标下的表现。结果表明性能差异明显，ConvNeXt-Tiny在马检测中达到95.53%的平均精度（AP），摩托车检测为89.12%。研究发现数据增强显著提升少数类检测能力，尤其对深层架构更为有效。本研究为不平衡二分类任务中的模型选择提供了依据，并量化了数据增强策略在缓解类别不平衡中的作用。
原文摘要 · Abstract (English)
This paper presents a comprehensive evaluation of nine convolutional neural network architectures for binary classification of horses and motorcycles in the VOC 2008 dataset. We address the significant class imbalance problem by implementing minority-class augmentation techniques. Our experiments compare modern architectures including ResNet-50, ConvNeXt-Tiny, DenseNet-121, and Vision Transformer across multiple performance metrics. Results demonstrate substantial performance variations, with ConvNeXt-Tiny achieving the highest Average Precision (AP) of 95.53% for horse detection and 89.12% for motorcycle detection. We observe that data augmentation significantly improves minority class detection, particularly benefiting deeper architectures. This study provides insights into architecture selection for imbalanced binary classification tasks and quantifies the impact of data augmentation strategies in mitigating class imbalance issues in object detection.
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