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Papers/EfficientSeg: An Efficient Semantic Segmentation Network

EfficientSeg: An Efficient Semantic Segmentation Network

Vahit Bugra Yesilkaynak, Yusuf H. Sahin, Gozde Unal

2020-09-14SegmentationSemantic Segmentation
PaperPDFCode(official)

Abstract

Deep neural network training without pre-trained weights and few data is shown to need more training iterations. It is also known that, deeper models are more successful than their shallow counterparts for semantic segmentation task. Thus, we introduce EfficientSeg architecture, a modified and scalable version of U-Net, which can be efficiently trained despite its depth. We evaluated EfficientSeg architecture on Minicity dataset and outperformed U-Net baseline score (40% mIoU) using the same parameter count (51.5% mIoU). Our most successful model obtained 58.1% mIoU score and got the fourth place in semantic segmentation track of ECCV 2020 VIPriors challenge.

Results

TaskDatasetMetricValueModel
Semantic SegmentationCityscapes VIPriors subsetAccuracy81.68EfficientSeg
Semantic SegmentationCityscapes VIPriors subsetmIoU58.03EfficientSeg
10-shot image generationCityscapes VIPriors subsetAccuracy81.68EfficientSeg
10-shot image generationCityscapes VIPriors subsetmIoU58.03EfficientSeg

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