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arXiv · 2210.09495

5th Place Solution to Kaggle Google Universal Image Embedding Competition

Noriaki Ota·Shingo Yokoi·Shinsuke Yamaoka
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Abstract

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

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Noriaki Ota, Shingo Yokoi, Shinsuke Yamaoka. 2022-10-18. 5th Place Solution to Kaggle Google Universal Image Embedding Competition. https://arxiv.org/abs/2210.09495

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