  Millions of stray animals suffer on the streets or are euthanized in shelters
every day around the world. In order to better adopt stray animals, scoring the
pawpularity (cuteness) of stray animals is very important, but evaluating the
pawpularity of animals is a very labor-intensive thing. Consequently, there has
been an urgent surge of interest to develop an algorithm that scores
pawpularity of animals. However, the dataset in Kaggle not only has images, but
also metadata describing images. Most methods basically focus on the most
advanced image regression methods in recent years, but there is no good method
to deal with the metadata of images. To address the above challenges, the paper
proposes an image regression model called PETS-SWINF that considers metadata of
the images. Our results based on a dataset of Kaggle competition,
"PetFinder.my", show that PETS-SWINF has an advantage over only based images
models. Our results shows that the RMSE loss of the proposed model on the test
dataset is 17.71876 but 17.76449 without metadata. The advantage of the
proposed method is that PETS-SWINF can consider both low-order and high-order
features of metadata, and adaptively adjust the weights of the image model and
the metadata model. The performance is promising as our leadboard score is
ranked 15 out of 3545 teams (Gold medal) currently for 2021 Kaggle competition
on the challenge "PetFinder.my".
