Overview
SegFormer is a semantic segmentation method leveraging transformers for efficient and powerful performance. It uses a hierarchical transformer encoder to produce multi-level features, combined with a lightweight all-MLP decoder. This architecture reduces computational complexity compared to existing transformer-based models. The primary value proposition is its ability to achieve state-of-the-art accuracy with reduced computational resources. It's implemented in PyTorch, using MMSegmentation as the codebase. Use cases include autonomous driving (Cityscapes dataset), scene understanding (ADE20K dataset), medical image analysis, and robotics. It's particularly useful where real-time performance and high accuracy are required simultaneously. The model is pre-trained on ImageNet-1K.