复现野生动物检测实验，验证预训练模型在新数据上的泛化能力
Reevaluating Automated Wildlife Species Detection: A Reproducibility Study on a Custom Image Dataset
- 用900张图像重现实验，使用与原研究不同的90个物种数据集
- 整体准确率62%，宏F1仅0.28，显示类别间性能差异大
- 提示需针对物种微调，才能获得稳定高精度结果
本研究重新评估了Carl等人对Google Inception-ResNet-v2模型在相机陷阱图像中自动检测欧洲野生哺乳动物的研究。为检验方法的可复现性与泛化能力，我们使用公开资源从头复现实验，采用包含900张图像、覆盖90个物种的新数据集。经最小预处理后，整体分类准确率达62%，与原研究报道的71%接近。与原研究一致，各类别表现差异显著，宏F1得分仅为0.28，表明当标签不直接对应ImageNet类别时，模型泛化能力受限。结果证实预训练卷积神经网络可作为野生动物识别的实用基线，但要实现一致高质量预测，仍需进行物种特异性适配或迁移学习。
原文摘要 · Abstract (English)
This study revisits the findings of Carl et al., who evaluated the pre-trained Google Inception-ResNet-v2 model for automated detection of European wild mammal species in camera trap images. To assess the reproducibility and generalizability of their approach, we reimplemented the experiment from scratch using openly available resources and a different dataset consisting of 900 images spanning 90 species. After minimal preprocessing, we obtained an overall classification accuracy of 62%, closely aligning with the 71% reported in the original work despite differences in datasets. As in the original study, per-class performance varied substantially, as indicated by a macro F1 score of 0.28,highlighting limitations in generalization when labels do not align directly with ImageNet classes. Our results confirm that pretrained convolutional neural networks can provide a practical baseline for wildlife species identification but also reinforce the need for species-specific adaptation or transfer learning to achieve consistent, high-quality predictions.
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