# VIPriors Object Detection Challenge

Source: [VIPriors Object Detection Challenge](<https://www.sota2.com/research/paper/68fa67f61e209131cea19732>)

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

Data updated: 2025-10-28T13:56:43.721Z

Authors: Zhipeng Luo, Lixuan Che

Published: 2020-07-16T08:10:42.000Z

## Abstract

This paper is a brief report to our submission to the VIPriors Object Detection Challenge. Object Detection has attracted many researchers' attention for its full application, but it is still a challenging task. In this paper, we study analysis the characteristics of the data, and an effective data enhancement method is proposed. We carefully choose the model which is more suitable for training from scratch. We benefit a lot from using softnms and model fusion skillfully.

## 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/2007.08170>)

[PDF](<https://arxiv.org/pdf/2007.08170.pdf>)