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
@article{arxiv.2210.09495,
title = {5th Place Solution to Kaggle Google Universal Image Embedding Competition},
author = {Noriaki Ota and Shingo Yokoi and Shinsuke Yamaoka},
journal= {arXiv preprint arXiv:2210.09495},
year = {2022}
}