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Millions of stray animals suffer on the streets or are euthanized in sheltersevery day around the world. In order to better adopt stray animals, scoring thepawpularity (cuteness) of stray animals is very important, but evaluating thepawpularity of animals is a very labor-intensive thing. Consequently, there hasbeen an urgent surge of interest to develop an algorithm that scorespawpularity of animals. However, the dataset in Kaggle not only has images, butalso metadata describing images. Most methods basically focus on the mostadvanced image regression methods in recent years, but there is no good methodto deal with the metadata of images. To address the above challenges, the paperproposes an image regression model called PETS-SWINF that considers metadata ofthe images. Our results based on a dataset of Kaggle competition,"PetFinder.my", show that PETS-SWINF has an advantage over only based imagesmodels. Our results shows that the RMSE loss of the proposed model on the testdataset is 17.71876 but 17.76449 without metadata. The advantage of theproposed method is that PETS-SWINF can consider both low-order and high-orderfeatures of metadata, and adaptively adjust the weights of the image model andthe metadata model. The performance is promising as our leadboard score isranked 15 out of 3545 teams (Gold medal) currently for 2021 Kaggle competitionon the challenge "PetFinder.my".

PETS-SWINF: A regression method that considers images with metadata  based Neural Network for pawpularity prediction on 2021 Kaggle Competition  "PetFinder.my"