  In this paper, we present our solution, which placed 5th in the kaggle Google
Universal Image Embedding Competition in 2022. We use the ViT-H visual encoder
of CLIP from the openclip repository as a backbone and train a head model
composed of BatchNormalization and Linear layers using ArcFace. The dataset
used was a subset of products10K, GLDv2, GPR1200, and Food101. And applying TTA
for part of images also improves the score. With this method, we achieve a
score of 0.684 on the public and 0.688 on the private leaderboard. Our code is
available.
https://github.com/riron1206/kaggle-Google-Universal-Image-Embedding-Competition-5th-Place-Solution
