  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.
