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.

翻译：在本报告中,我们对2020年3月至7月发生的ECCV 2020 V V VVIPR目标探测挑战的处理方式有所否认,我们表明,通过使用最新数据增强战略、模型设计和后处理合用方法,有可能克服数据短缺的困难并取得竞争性结果,值得注意的是,我们的总体检测系统在COCO 2017验证系统上只使用10K个培训图像,而没有培训前或转让学习权重,在挑战中排在第二位。