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SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference

Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zheng-Ning Liu, Ming-Ming Cheng and Shi-Min Hu. "SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation" arXiv preprint arXiv:2207.13600 (2022).

Performance

Cityscapes

Model Backbone Resolution Training Iters mIoU mIoU (flip) mIoU (ms+flip) Links
SegNeXt MSCAN_T 1024x1024 160000 81.04% 81.20% 81.43% model |log | vdl
SegNeXt MSCAN_S 1024x1024 160000 81.33% 81.44% 81.47% model | log | vdl
SegNeXt MSCAN_B 1024x1024 160000 82.74% 82.84% 83.01% model | log | vdl
SegNeXt MSCAN_L 1024x1024 160000 83.32% 83.38% 83.60% model | log | vdl

Note: In the current implementation, we found some potential issues that could cause training of SegNeXt with backbone MSCAN_T (denoted as SegNeXt-MSCAN_T) to crash when using the multi-card training setup from the original paper. As a work around, we did not use different settings of learning rate and weight decay for different layers. At the same time, we amplified the global learning rate by 10 times. With this setup, we obtained the above results of SegNeXt-MSCAN_T.