CV 3315 Is All You Need : Semantic Segmentation Competition

• URL: http://arxiv.org/abs/2206.12571v1
• Date: Sat, 25 Jun 2022 06:27:57 GMT
• Title: CV 3315 Is All You Need : Semantic Segmentation Competition
• Authors: Akide Liu, Zihan Wang
• Abstract summary: This competition focus on Urban-Sense based on the vehicle camera view. Class highly unbalanced Urban-Sense images dataset challenge the existing solutions. Deep Conventional neural network-based semantic segmentation methods become flexible solutions applicable to real-world applications.
• Score: 14.818852884385015
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This competition focus on Urban-Sense Segmentation based on the vehicle camera view. Class highly unbalanced Urban-Sense images dataset challenge the existing solutions and further studies. Deep Conventional neural network-based semantic segmentation methods such as encoder-decoder architecture and multi-scale and pyramid-based approaches become flexible solutions applicable to real-world applications. In this competition, we mainly review the literature and conduct experiments on transformer-driven methods especially SegFormer, to achieve an optimal trade-off between performance and efficiency. For example, SegFormer-B0 achieved 74.6% mIoU with the smallest FLOPS, 15.6G, and the largest model, SegFormer- B5 archived 80.2% mIoU. According to multiple factors, including individual case failure analysis, individual class performance, training pressure and efficiency estimation, the final candidate model for the competition is SegFormer- B2 with 50.6 GFLOPS and 78.5% mIoU evaluated on the testing set. Checkout our code implementation at https://vmv.re/cv3315.

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