How To Train SegFormer on a Custom Dataset

SegFormer, a new state-of-the-art semantic segmentation algorithm, is introduced as an advancement in transformer models, offering innovations that address previous limitations like low resolution and high computational cost. This blog post details a step-by-step guide on using Pytorch Lightning to train SegFormer on a custom dataset, specifically utilizing the public Balloons dataset while highlighting the process of creating a semantic segmentation dataset and defining a Pytorch Lightning model class. SegFormer distinguishes itself with a unique "positional-encoding-free and hierarchical Transformer encoder" and a "lightweight All-MLP decoder design," enabling it to operate at arbitrary resolutions and efficiently combine local and global attention for high-quality representations. The guide also covers evaluating the model on a test dataset, visualizing results, and the potential applications of semantic segmentation in fields like self-driving cars and robotics. The tutorial concludes with instructions on training, testing, and visualizing model outputs in TensorBoard, emphasizing the algorithm's efficiency and effectiveness in various semantic segmentation tasks.