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.

翻译：本文针对VOC 2008数据集中的马与摩托车二分类任务，对九种卷积神经网络架构进行了系统评估。通过实施少数类数据增强技术，我们解决了显著的类别不平衡问题。实验比较了包括ResNet-50、ConvNeXt-Tiny、DenseNet-121和Vision Transformer在内的现代架构在多项性能指标上的表现。结果显示各架构性能存在显著差异，其中ConvNeXt-Tiny在马类检测中取得最高平均精度（AP）95.53%，在摩托车检测中达到89.12%。我们观察到数据增强能显著提升少数类检测性能，尤其对深层架构更为有益。本研究为不平衡二分类任务的架构选择提供了参考依据，并量化了数据增强策略在缓解目标检测中类别不平衡问题的作用。