PaddleSeg/configs/rtformer

• RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
• • Reference
  • Performance
  • • Cityscapes
    • ADE20k

RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

Reference

Wang, Jian, Chenhui Gou, Qiman Wu, Haocheng Feng, Junyu Han, Errui Ding, and Jingdong Wang. "RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer." arXiv preprint arXiv:2210.07124 (2022).

Performance

Cityscapes

Model	Backbone	Resolution	Training Iters	mIoU	mIoU (flip)	mIoU (ms+flip)	Links
RTFormer-Base	-	1024x512	120000	79.24%	79.80%	80.19%	model | log | vdl
RTFormer-Slim	-	1024x512	120000	76.31%	77.05%	77.58%	model | log | vdl

ADE20k

Model	Backbone	Resolution	Training Iters	mIoU	mIoU (flip)	mIoU (ms+flip)	Links
RTFormer-Base	-	512x512	160000	42.02%	42.43%	42.72%	model | log | vdl
RTFormer-Slim	-	512x512	160000	36.67%	37.32%	37.20%	model | log | vdl