论文标题
ECCV 2020 VIPRIORS对象检测挑战的第二名解决方案
2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge
论文作者
论文摘要
在本报告中，我们将我们对eCCV 2020 Vipriors对象检测挑战的方法进行了降级。我们表明，通过使用最先进的数据增强策略，模型设计和后处理集合方法，可以克服数据短暂的难度并获得有竞争力的结果。值得注意的是，我们的整体检测系统在2017年可可验证设置中仅使用10K培训图像而无需在挑战中排名第二。
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.
