LostPaw：使用基于对比学习的Transformer与视觉输入寻找走失宠物
LostPaw: Finding Lost Pets using a Contrastive Learning-based Transformer with Visual Input
AI总结：
本文提出LostPaw框架，利用对比学习Transformer模型识别宠物图像，在狗图像数据集上经3折交叉验证达到90%测试准确率，为走失宠物寻找Web应用奠定基础。
摘要：
宠物走失会给宠物主人带来极大的痛苦，而寻找走失宠物往往既困难又耗时。基于人工智能的应用可以显著提高寻找走失宠物的速度和准确性。为了支持此类应用，本研究引入了一种对比神经网络模型，该模型能够准确区分宠物图像。该模型在一个大型狗图像数据集上进行训练，并通过3折交叉验证进行评估。经过350个轮次的训练后，模型达到了90%的测试准确率。此外，由于测试准确率与训练准确率非常接近，避免了过拟合。我们的研究结果表明，对比神经网络模型有望成为定位走失宠物的工具。本文提出了一个潜在Web应用的基础框架，该应用旨在帮助用户定位其失踪的宠物。该应用将允许用户上传走失宠物的图像，并在其图像数据库中识别到匹配图像时提供通知。这一功能旨在提高宠物主人寻找并与他们心爱的动物团聚的效率和准确性。
英文摘要：
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. 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 presents the foundational framework for a potential web application designed to assist users in locating their missing pets. The application will allow users to upload images of their lost pets and provide notifications when matching images are identified within its image database. This functionality aims to enhance the efficiency and accuracy with which pet owners can search for and reunite with their beloved animals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
