Kern Daria, Schiele Tobias, Klauck Ulrich, Ingabire Winfred
Faculty Electronics & Computer Science, Aalen University, 73430 Aalen, Germany.
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.
Animals (Basel). 2024 Dec 24;15(1):1. doi: 10.3390/ani15010001.
The chicken is the world's most farmed animal. In this work, we introduce the Chicks4FreeID dataset, the first publicly available dataset focused on the reidentification of individual chickens. We begin by providing a comprehensive overview of the existing animal reidentification datasets. Next, we conduct closed-set reidentification experiments on the introduced dataset, using transformer-based feature extractors in combination with two different classifiers. We evaluate performance across domain transfer, supervised, and one-shot learning scenarios. The results demonstrate that transfer learning is particularly effective with limited data, and training from scratch is not necessarily advantageous even when sufficient data are available. Among the evaluated models, the vision transformer paired with a linear classifier achieves the highest performance, with a mean average precision of 97.0%, a top-1 accuracy of 95.1%, and a top-5 accuracy of 100.0%. Our evaluation suggests that the vision transformer architecture produces higher-quality embedding clusters than the Swin transformer architecture. All data and code are publicly shared under a CC BY 4.0 license.
鸡是世界上养殖最多的动物。在这项工作中，我们引入了Chicks4FreeID数据集，这是首个专注于个体鸡重新识别的公开可用数据集。我们首先全面概述了现有的动物重新识别数据集。接下来，我们在引入的数据集上进行闭集重新识别实验，使用基于Transformer的特征提取器与两种不同的分类器相结合。我们评估了跨域迁移、监督学习和一次性学习场景下的性能。结果表明，迁移学习在数据有限时特别有效，即使有足够的数据，从头开始训练也不一定有优势。在所评估的模型中，与线性分类器配对的视觉Transformer实现了最高性能，平均精度为97.0%，top-1准确率为95.1%，top-5准确率为100.0%。我们的评估表明，视觉Transformer架构比Swin Transformer架构产生更高质量的嵌入簇。所有数据和代码都根据CC BY 4.0许可公开共享。
