ZhuoYi

Introduction

Semantic segmentation using CNN requires large volume and high quality of the training dataset to achieve high performance. These requirements pose challenges to storage and compute hardware resources. However, lower quality dataset induces unwanted artifacts that may destroy the important image information. To solve these challenges, we need to better understand how the quality of training data affects the semantic segmentation algorithm performance. The goal of this project is to see how training data quality affects semantic segmentation network performance.

Appendix

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