Update card: RDD-val per-class metrics + full 5-model comparison + links; upload weights

README.md

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+---
+license: agpl-3.0
+library_name: ultralytics
+pipeline_tag: object-detection
+tags:
+- yolo
+- yolo26
+- road-damage
+- pothole-detection
+- crack-detection
+- knowledge-distillation
+model-index:
+- name: YOLO26s_RDD_FRDC_Distilled_v2
+  results:
+  - task: {type: object-detection, name: Road Damage Detection}
+    dataset: {name: Unified Road Defect Dataset (held-out val, 4509 imgs), type: TamAko783/Unified_Road_Defect_Dataset}
+    metrics:
+    - {type: mAP50, value: 0.692, name: mAP@50}
+    - {type: mAP50-95, value: 0.375, name: mAP@50-95}
+    - {type: F1, value: 0.672, name: F1}
+---
+# YOLO26s · Distilled v2 (small, 2 teachers)
+
+A **YOLO26s** road-damage detector (4-class CRDDC: D00 longitudinal, D10 transverse,
+D20 alligator, D40 pothole) on the [Unified Road Defect Dataset](https://huggingface.co/datasets/TamAko783/Unified_Road_Defect_Dataset).
+
+**Method:** **Semi-supervised distillation, two teachers** (Co-DETR + RTMDet, WBF) on the larger YOLO26s student. Best overall model in the suite.
+
+## Metrics — RDD held-out validation (4,509 images, 11,470 boxes)
+| Class | mAP@50 | mAP@50-95 | Precision | Recall | F1 |
+|---|---:|---:|---:|---:|---:|
+| D00 Longitudinal | 0.635 | 0.357 | 0.720 | 0.561 | 0.630 |
+| D10 Transverse | 0.664 | 0.345 | 0.715 | 0.589 | 0.646 |
+| D20 Alligator | 0.727 | 0.409 | 0.743 | 0.647 | 0.692 |
+| D40 Pothole | 0.741 | 0.390 | 0.774 | 0.668 | 0.717 |
+| **Overall** | **0.692** | **0.375** | **0.738** | **0.616** | **0.672** |
+
+## Full model comparison (same held-out val)
+All five models in this study, evaluated identically (imgsz 640):
+
+| Model | Variant | Params | Distillation | mAP@50 | mAP@50-95 | F1 |
+|---|---|---:|---|---:|---:|---:|
+| [YOLO26n_RDD_Base](https://huggingface.co/TamAko783/YOLO26n_RDD_Base) | YOLO26n | 2.4M | — (GT only) | 0.635 | 0.334 | 0.621 |
+| [YOLO26n_RDD_FRDC_Distilled](https://huggingface.co/TamAko783/YOLO26n_RDD_FRDC_Distilled) | YOLO26n | 2.4M | 1 teacher (Co-DETR) | 0.640 | 0.337 | 0.625 |
+| [YOLO26n_RDD_FRDC_Distilled_v2](https://huggingface.co/TamAko783/YOLO26n_RDD_FRDC_Distilled_v2) | YOLO26n | 2.4M | 2 teachers (Co-DETR+RTMDet) | 0.638 | 0.337 | 0.626 |
+| [YOLO26s_RDD_Base](https://huggingface.co/TamAko783/YOLO26s_RDD_Base) | YOLO26s | 9M | — (GT only) | 0.687 | 0.372 | 0.665 |
+| **➤ YOLO26s_RDD_FRDC_Distilled_v2** (this model) | YOLO26s | 9M | 2 teachers (Co-DETR+RTMDet) | **0.692** | **0.375** | **0.672** |
+
+**Reading it:**
+- **Distillation helps** — every distilled model beats its GT-only baseline.
+- **Capacity helps most** — the YOLO26s models (+~0.05 mAP@50) clearly outperform the nano ones on this val.
+- **One vs two teachers** is a near-tie at nano size; the two-teacher set's edge is small.
+- **Cross-domain check (independent RDDC2024-ID, Indonesia, 8,901 imgs):** distilled models
+  generalized *better* than baselines, while the larger GT-only model generalized *worse* —
+  evidence the distillation's added data improves robustness, not just in-domain fit.
+
+> RDD ground truth has known missing annotations, so absolute precision/recall are conservative
+> for all models. The comparison is fair — every model uses the identical held-out val, never trained on.
+
+## Usage
+```python
+from ultralytics import YOLO
+model = YOLO("YOLO26s_RDD_FRDC_Distilled_v2.pt")
+results = model("road.jpg")
+```
+
+## Models in this suite
+- [YOLO26n_RDD_Base](https://huggingface.co/TamAko783/YOLO26n_RDD_Base) · [YOLO26n_RDD_FRDC_Distilled (v1)](https://huggingface.co/TamAko783/YOLO26n_RDD_FRDC_Distilled) · [YOLO26n_RDD_FRDC_Distilled_v2](https://huggingface.co/TamAko783/YOLO26n_RDD_FRDC_Distilled_v2)
+- [YOLO26s_RDD_Base](https://huggingface.co/TamAko783/YOLO26s_RDD_Base) · [YOLO26s_RDD_FRDC_Distilled_v2](https://huggingface.co/TamAko783/YOLO26s_RDD_FRDC_Distilled_v2)
+
+## Datasets
+- Base: [TamAko783/Unified_Road_Defect_Dataset](https://huggingface.co/datasets/TamAko783/Unified_Road_Defect_Dataset)
+- Distillation sets: [v1](https://huggingface.co/datasets/TamAko783/Unified_Road_Defect_FRDC_Pseudolabeled) ·
+  [v2 (two-teacher)](https://huggingface.co/datasets/TamAko783/Unified_Road_Defect_FRDC_Pseudolabeled_v2)
+
+## Credits
+- Datasets: RDD-2022 (Arya et al.) · UAV-PDD2023 · RoadDamageVision (Silva Zendron & Leithardt, CC BY 4.0).
+- Distillation teachers: **Co-DETR Swin-L + RTMDet-x** — FRDC (Wang Fangjun et al.), ORDDC'2024 winner.
+- Student: YOLO26s, AGPL-3.0 (Ultralytics).

YOLO26s_RDD_FRDC_Distilled_v2.pt

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