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

2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge