What is SegFormer?

SegFormer is a computer vision framework used in semantic segmentation tasks, implemented with transformers.

About the model

Here is an overview of the

SegFormer

model:

Date of Release May 31, 2021
Model Type Semantic Segmentation
Architecture Transformers
Framework Used PyTorch
Annotation Format
Stars on GitHub 1300+

With ViT as a backbone showing great promise, various papers began to build on the idea and innovate to address issues of low resolution and high computational cost. And, while performance continued to improve with each new method, these papers seemed to focus solely on the design of the transformer encoder and neglected the decoder. Enter SegFormer. SegFormer sets itself apart with:

  • a new "positional-encoding-free and hierarchical Transformer encoder"
  • "a lightweight All-MLP decoder design"

The novel encoder is able operate at arbitrary resolutions without impacting performance. Additionally, the encoder is able to generate both high resolution and low resolution features in contrast to ViT. The decoder design is able to combine both local and global attention to produce high quality representations at low cost.

With these novel improvements, SegFormer sets a new SOTA on ADE20K, Cityscapes, and COCO-Stuff semantic segmentation datasets.

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Model Performance

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SegFormer Annotation Format

SegFormer

uses the

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

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