Related papers: Data-Efficient Instance Segmentation with a Single GPU

Data-Efficient Instance Segmentation with a Single GPU

URL: http://arxiv.org/abs/2110.00242v1
Date: Fri, 1 Oct 2021 07:36:20 GMT
Title: Data-Efficient Instance Segmentation with a Single GPU
Authors: Pengyu Chen, Wanhua Li, Jiwen Lu
Abstract summary: We introduce a data-efficient segmentation method we used in the 2021 VIPriors Instance Challenge. Our solution is a modified version of Swin Transformer, based on the mmdetection which is a powerful toolbox. Our method achieved the AP@0.50:0.95 (medium) of 0.592, which ranks second among all contestants.
Score: 88.31338435907304
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Not everyone is wealthy enough to have hundreds of GPUs or TPUs. Therefore, we've got to find a way out. In this paper, we introduce a data-efficient instance segmentation method we used in the 2021 VIPriors Instance Segmentation Challenge. Our solution is a modified version of Swin Transformer, based on the mmdetection which is a powerful toolbox. To solve the problem of lack of data, we utilize data augmentation including random flip and multiscale training to train our model. During inference, multiscale fusion is used to boost the performance. We only use a single GPU during the whole training and testing stages. In the end, our team named THU_IVG_2018 achieved the result of 0.366 for AP@0.50:0.95 on the test set, which is competitive with other top-ranking methods while only one GPU is used. Besides, our method achieved the AP@0.50:0.95 (medium) of 0.592, which ranks second among all contestants

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