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Survey of semantic segmentation in urban scenes with CV-3315-Is-All-You-Need

Survey of semantic segmentation in urban scenes with CV-3315-Is-All-You-Need


CV 3315 Is All You Need : Semantic Segmentation Competition



... 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.


... 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 ...



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