Pt. 2 — YOLO26-Seg is wild:
→ Distribution Focal Loss removed
→ MuSGD optimizer (hybrid borrowed from LLM training)
→ NMS baked into the model
→ Boundary-aware supervision = razor-sharp masks
→ Up to 43% faster on CPU
→ One ONNX export → Pi, drone, phone
Deep dive:… pic.twitter.com/lSQmbRd1L0

— Satya Mallick (@LearnOpenCV) 14 mai 2026

Pt. 2 — YOLO26-Seg is wild:
→ Distribution Focal Loss removed
→ MuSGD optimizer (hybrid borrowed from LLM training)
→ NMS baked into the model
→ Boundary-aware supervision = razor-sharp masks
→ Up to 43% faster on CPU
→ One ONNX export → Pi, drone, phone
Deep dive:

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