  Deep convolution neural network has attracted many attentions in large-scale
visual classification task, and achieves significant performance improvement
compared to traditional visual analysis methods. In this paper, we explore many
kinds of deep convolution neural network architectures for large-scale product
recognition task, which is heavily class-imbalanced and noisy labeled data,
making it more challenged. Extensive experiments show that PNASNet achieves
best performance among a variety of convolutional architectures. Together with
ensemble technology and negative learning loss for noisy labeled data, we
further improve the model performance on online test data. Finally, our
proposed method achieves 0.1515 mean top-1 error on online test data.
