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A YOLO26n road-damage detector (4-class CRDDC: D00 longitudinal, D10 transverse, D20 alligator, D40 pothole) on the Unified Road Defect Dataset.
Model source
Source description
A YOLO26n road-damage detector (4-class CRDDC: D00 longitudinal, D10 transverse, D20 alligator, D40 pothole) on the Unified Road Defect Dataset.
Method: Semi-supervised distillation, two teachers. Co-DETR + RTMDet pseudo-labels fused with Weighted Boxes Fusion; student trained on GT + fused pseudo-labels.
Sources
1 sourceVerified Sep 9
Model artifacts
1 artifactSource excerpts
2 excerpts| Class | mAP@50 | mAP@50-95 | Precision | Recall | F1 |
|---|---|---|---|---|---|
| D00 Longitudinal | 0.589 | 0.323 | 0.687 | 0.511 | 0.586 |
| D10 Transverse | 0.594 | 0.301 | 0.683 | 0.519 | 0.590 |
| D20 Alligator | 0.685 | 0.376 | 0.720 | 0.603 | 0.656 |
| D40 Pothole | 0.684 | 0.347 | 0.735 | 0.614 | 0.669 |
| Overall |
All five models in this study, evaluated identically (imgsz 640):
| Model | Variant | Params | Distillation | mAP@50 | mAP@50-95 | F1 |
|---|---|---|---|---|---|---|
| YOLO26n_RDD_Base | YOLO26n | 2.4M | — (GT only) | 0.635 | 0.334 | 0.621 |
| YOLO26n_RDD_FRDC_Distilled | YOLO26n | 2.4M | 1 teacher (Co-DETR) | 0.640 | 0.337 | 0.625 |
| ➤ YOLO26n_RDD_FRDC_Distilled_v2 (this model) | YOLO26n | 2.4M | 2 teachers (Co-DETR+RTMDet) | 0.638 | 0.337 | 0.626 |
Reading it:
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.
from ultralytics import YOLO
model = YOLO("YOLO26n_RDD_FRDC_Distilled_v2.pt")
results = model("road.jpg")
--- 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: YOLO26n_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: 0.638, name: mAP@50} - {type: mAP50-95, value: 0.337, name: mAP@50-95} - {type: F1, value: 0.626, name: F1} --- # YOLO26n · Distilled v2 (2 teachers: Co-DETR + RTMDet) A **YOLO26n** 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 pseudo-labels fused with Weighted Boxes Fusion; student trained on GT + fused pseudo-labels. ## Metrics — RDD held-out validation (4,509 images, 11,470 boxes) | Class | mAP@50 | mAP@50-95 | Precision | Recall | F1 | |---|---:|---:|---:|---:|---:| | D00 Longitudinal | 0.589 | 0.323 | 0.687 | 0.511 | 0.586 | | D10 Transverse | 0.594 | 0.301 | 0.683 | 0.519 | 0.590 | | D20 Alligator | 0.685 | 0.376 | 0.720 | 0.603 | 0.656 | | D40 Pothole | 0.684 | 0.347 | 0.735 | 0.614 | 0.669 | | **Overall** | **0.638** | **0.337** | **0.706** | **0.562** | **0.626** | ## 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** (this model) | 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](https://huggingface.co/TamAko783/YOLO26s_RDD_FRDC_Distilled_v2) | YOLO26s | 9M | 2 teachers (Co-DETR+RTMDet) | 0.69...
Source context: 10 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo TamAko783/YOLO26n_RDD_FRDC_Distilled_v2
| 0.337 |
| 0.706 |
| 0.562 |
| 0.626 |
| YOLO26s |
| 9M |
| — (GT only) |
| 0.687 |
| 0.372 |
| 0.665 |
| YOLO26s_RDD_FRDC_Distilled_v2 | YOLO26s | 9M | 2 teachers (Co-DETR+RTMDet) | 0.692 | 0.375 | 0.672 |