# 2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge

Source: [2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge](<https://www.sota2.com/research/paper/68fa67f81e209131cea19840>)

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

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

Authors: Yinzheng Gu, Yihan Pan, Shizhe Chen

Published: 2020-07-17T09:21:29.000Z

## Abstract

In this report, we descibe our approach to the ECCV 2020 VIPriors Object Detection Challenge which took place from March to July in 2020. We show that by using state-of-the-art data augmentation strategies, model designs, and post-processing ensemble methods, it is possible to overcome the difficulty of data shortage and obtain competitive results. Notably, our overall detection system achieves 36.6$\\%$ AP on the COCO 2017 validation set using only 10K training images without any pre-training or transfer learning weights ranking us 2nd place in the challenge.

## 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/2007.08849>)

[PDF](<https://arxiv.org/pdf/2007.08849.pdf>)