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Solution for Large-scale Long-tailed Recognition with Noisy Labels

2021-06-20 12:09:38
Yuqiao Xian, Jia-Xin Zhuang, Fufu Yu
arXiv_AI
         
Abstract

This is a technical report for CVPR 2021 AliProducts Challenge. AliProducts Challenge is a competition proposed for studying the large-scale and fine-grained commodity image recognition problem encountered by worldleading ecommerce companies. The large-scale product recognition simultaneously meets the challenge of noisy annotations, imbalanced (long-tailed) data distribution and fine-grained classification. In our solution, we adopt stateof-the-art model architectures of both CNNs and Transformer, including ResNeSt, EfficientNetV2, and DeiT. We found that iterative data cleaning, classifier weight normalization, high-resolution finetuning, and test time augmentation are key components to improve the performance of training with the noisy and imbalanced dataset. Finally, we obtain 6.4365% mean class error rate in the leaderboard with our ensemble model.

Abstract (translated)

URL

https://arxiv.org/abs/2106.10683

PDF

https://arxiv.org/pdf/2106.10683.pdf

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