  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.
