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

arXiv: https://arxiv.org/abs/2007.08849 · HTML: https://mlsanity.com/paper/2007.08849/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges](https://mlsanity.com/paper/2103.03768/) (2021)
- [Data-Efficient Challenges in Visual Inductive Priors: A Retrospective](https://mlsanity.com/paper/2506.08612/) (2025)
- [Frozen High-Resolution Inference for Cross-City Object Detection: An AI City Challenge 2026 Study](https://mlsanity.com/paper/2608.03136/) (2026)

## Abstract (arXiv)

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.