D3-UNet-v5 combines a CNN encoder with DINOv3 vision transformer features for multi-scale semantic segmentation on high-resolution imagery like Cityscapes datasets. The architecture processes 1024×2048 input images through four progressive encoder stages (64–512 channels), fuses DINOv3 ViT patch tokens via multi-scale projection, and reconstructs dense predictions through an ASPP bottleneck and symmetric decoder with skip connections. This hybrid CNN-ViT design leverages self-supervised transformer knowledge to improve segmentation accuracy while maintaining spatial precision across 19 semantic classes. Fork this diagram on Diagrams.so to customize encoder depths, fusion strategies, or adapt the architecture for your own vision datasets and OCI ML infrastructure. The SE Block squeeze-and-excitation modules enhance channel-wise feature recalibration, critical for balancing multi-scale information in dense prediction tasks.