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

5th Place Solution to Kaggle Google Universal Image Embedding Competition