# A Technical Report for VIPriors Image Classification Challenge

Source: [A Technical Report for VIPriors Image Classification Challenge](<https://www.sota2.com/research/paper/68fa67f81e209131cea1981c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:27:00.192Z

Authors: Zhipeng Luo, Ge Li, Zhiguang Zhang

Published: 2020-07-17T02:30:09.000Z

## Abstract

Image classification has always been a hot and challenging task. This paper is a brief report to our submission to the VIPriors Image Classification Challenge. In this challenge, the difficulty is how to train the model from scratch without any pretrained weight. In our method, several strong backbones and multiple loss functions are used to learn more representative features. To improve the models' generalization and robustness, efficient image augmentation strategies are utilized, like autoaugment and cutmix. Finally, ensemble learning is used to increase the performance of the models. The final Top-1 accuracy of our team DeepBlueAI is 0.7015, ranking second in the leaderboard.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.08722>)

[PDF](<https://arxiv.org/pdf/2007.08722.pdf>)