This competition 1 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 pyramidbased approaches become flexible solutions applicable to real-world applications. In this competition, we mainly review the literature and conduct experiments on transformerdriven methods especially SegFormer [26], 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 2 .