Abstract
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.
Abstract (translated)
这项研究重新审视了Carl等人对Google的Inception-ResNet-v2预训练模型在自动检测欧洲野生哺乳动物种类于相机陷阱图像中的应用的研究成果。为了评估其方法的可重复性和泛化能力，我们从零开始使用公开资源和一个包含900张图片、涵盖90个物种的新数据集重新实施了该实验。经过最少的数据预处理后，我们得到了62%的整体分类准确率，这一结果与原研究报道的71%非常接近，尽管所用的数据集有所不同。正如原研究中所述，每个类别的性能差异显著，这一点通过宏观F1分数为0.28得以体现，这表明当标签不能直接对应于ImageNet类别时，模型在泛化上存在局限性。 我们的结果证实了预训练的卷积神经网络可以作为野生动物物种识别的实际基准，但也强调了为了获得一致且高质量的预测，需要进行特定物种的适应或迁移学习。
URL
https://arxiv.org/abs/2512.07305
