PetsRS - a Dataset and Benchmark for Pet Recognition on a Climate Disaster Scenario

In the first half of 2024, thousands of pets were separated from their guardians due to the floods in Rio Grande do Sul, Brazil. Tools that help guardians search for their lost animals are pivotal in such situations. Such a tool might allow users (in this case, guardians or volunteers from shelters) to send images of the animals, allowing manual or automated searches to match lost and rescued animals. This work presents a novel dataset – the PetsRS dataset¹ – containing organized tuples of pictures of dogs and cats for pet retrieval. We show pet recognition results using pre-trained off-the-shelf image encoders and the K-NN algorithm with embeddings as data points. In our results, DINOv2 achieves the best recall@K on all tests. This work aims to create a benchmark and baseline for evaluating techniques that tackle pet recognition.