Code Panoptic Image Segmentation with Vision Transformer and Mask2Former - A PyTorch Tutorial

Learn to implement panoptic image segmentation in a PyTorch tutorial that explores the innovative Mask2Former architecture and Vision Transformers. Discover how the Transformer decoder generates binary masks and classes in parallel, building upon MaskFormer's successful "binary mask classification" paradigm for semantic segmentation. Explore the key architectural improvements that enable instance segmentation, including masked attention which constrains cross-attention within predicted mask regions. Understand how these advances have led to "universal image segmentation" architectures that can handle any segmentation task while achieving state-of-the-art results - 57.8 PQ on COCO for panoptic segmentation, 50.1 AP on COCO for instance segmentation, and 57.7 mIoU on ADE20K for semantic segmentation. Based on research from the Hugging Face blog and the groundbreaking paper "Masked-attention Mask Transformer for Universal Image Segmentation" by Cheng et al.