SegFormer is a semantic segmentation framework and transformer-based model designed for pixel-level image classification. It provides a deep learning architecture that assigns class labels to pixels using a hierarchical transformer encoder and a multi-layer perceptron decoder.
The framework utilizes a hierarchical transformer encoder to process multi-scale features through a pyramid of blocks and an all-MLP decoder to aggregate these features without complex attention mechanisms. It incorporates overlap patch embedding to preserve local continuity and sequential self-attention reduction to manage computational costs.
The project covers the full lifecycle of computer vision model development, including GPU-accelerated training, model evaluation using pixel-level accuracy metrics, and inference for generating color-coded visualization maps. It includes utilities for loading pretrained weights and checkpoints to facilitate scene understanding and object masking.
A Dockerized runtime environment is provided to ensure reproducible deployment and consistent execution of the model and its dependencies.
