  We present SegFormer, a simple, efficient yet powerful semantic segmentation
framework which unifies Transformers with lightweight multilayer perception
(MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a
novel hierarchically structured Transformer encoder which outputs multiscale
features. It does not need positional encoding, thereby avoiding the
interpolation of positional codes which leads to decreased performance when the
testing resolution differs from training. 2) SegFormer avoids complex decoders.
The proposed MLP decoder aggregates information from different layers, and thus
combining both local attention and global attention to render powerful
representations. We show that this simple and lightweight design is the key to
efficient segmentation on Transformers. We scale our approach up to obtain a
series of models from SegFormer-B0 to SegFormer-B5, reaching significantly
better performance and efficiency than previous counterparts. For example,
SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters, being 5x
smaller and 2.2% better than the previous best method. Our best model,
SegFormer-B5, achieves 84.0% mIoU on Cityscapes validation set and shows
excellent zero-shot robustness on Cityscapes-C. Code will be released at:
github.com/NVlabs/SegFormer.
