Losing pets can be highly distressing for pet owners, and finding a lost pet is often challenging and time-consuming. An artificial intelligence-based application can significantly improve the speed and accuracy of finding lost pets. In order to facilitate such an application, this study introduces a contrastive neural network model capable of accurately distinguishing between images of pets. The model was trained on a large dataset of dog images and evaluated through 3-fold cross-validation. Following 350 epochs of training, the model achieved a test accuracy of 90%. Furthermore, overfitting was avoided, as the test accuracy closely matched the training accuracy. Our findings suggest that contrastive neural network models hold promise as a tool for locating lost pets. This paper provides the foundation for a potential web application that allows users to upload images of their missing pets, receiving notifications when matching images are found in the application's image database. This would enable pet owners to quickly and accurately locate lost pets and reunite them with their families.

翻译：宠物走失常给主人带来极大痛苦，而寻找失宠往往既困难又耗时。基于人工智能的应用可显著提升搜寻失宠的速度与准确性。为促进此类应用的开发，本研究提出一种对比神经网络模型，能够准确区分宠物图像。该模型基于大规模狗图像数据集进行训练，并通过三折交叉验证进行评估。经过350轮训练后，模型测试准确率达到90%。此外，由于测试准确率与训练准确率高度吻合，有效避免了过拟合现象。研究结果表明，对比神经网络模型作为寻找失宠的工具具有应用潜力。本文为潜在的网络应用奠定基础——用户可上传失踪宠物图像，当应用图像数据库中发现匹配图像时将接收通知。这将使宠物主人能够快速准确定位失宠，促使其与家人团聚。