road-scene-ddrnet23slim-camvid

DDRNet-23-slim — Road Scene Semantic Segmentation (CamVid)

Real-time semantic segmentation for autonomous driving road scenes, trained on CamVid.

Model

• Architecture: DDRNet-23-slim (dual-resolution, DAPPM context module)
• Params: 5.88M
• Input: 3x640x640 RGB, ImageNet-normalized
• Output: 32-class per-pixel logits

Training

• Dataset: CamVid (600 train / 101 val images)
• Loss: weighted CrossEntropy (inverse class frequency), Void as ignore_index
• Optimizer: AdamW, cosine LR schedule
• Epochs: 50

Results (val set, full confusion-matrix mIoU)

• Mean IoU: 0.3334 (over classes present in val set)

Known limitation

6 classes (Animal, LaneMkgsNonDriv, ParkingBlock, RoadShoulder, SUVPickupTruck, TrafficCone) score 0 IoU — each has <0.5% pixel frequency in the 600-image training set. This is a data-scarcity ceiling, not a training deficiency; more epochs do not resolve it. Cityscapes or Mapillary Vistas (larger, more balanced datasets) would be required to learn these classes reliably.

Per-class IoU