Du hast einen ComfyUI-Workflow? Erstelle direkt im Browser eine verständliche Workflow-Dokumentation.Workflow verständlich machen.Jetzt ausprobieren ↗
A YOLO26n road-damage detector (4-class CRDDC: D00 longitudinal, D10 transverse, D20 alligator, D40 pothole) on the Unified Road Defect Dataset.
Modellquelle
Quellenbeschreibung
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, single teacher. Co-DETR (Swin-L) pseudo-labeled the unlabeled RDD-2022 test set; student trained on GT + pseudo-labels.
Quellen
1 QuelleVerifiziert 9. Sept.
Modellartefakte
1 ArtefaktQuellenauszüge
2 Auszüge| Class | mAP@50 | mAP@50-95 | Precision | Recall | F1 |
|---|---|---|---|---|---|
| D00 Longitudinal | 0.590 | 0.324 | 0.675 | 0.520 | 0.588 |
| D10 Transverse | 0.594 | 0.299 | 0.683 | 0.504 | 0.580 |
| D20 Alligator | 0.687 | 0.375 | 0.713 | 0.617 | 0.662 |
| D40 Pothole | 0.688 | 0.347 | 0.737 | 0.614 | 0.670 |
| 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 (this model) | YOLO26n | 2.4M | 1 teacher (Co-DETR) | 0.640 | 0.337 | 0.625 |
| YOLO26n_RDD_FRDC_Distilled_v2 | 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.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 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: 0.640, name: mAP@50} - {type: mAP50-95, value: 0.337, name: mAP@50-95} - {type: F1, value: 0.625, name: F1} --- # YOLO26n · Distilled v1 (1 teacher: Co-DETR) 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, single teacher.** Co-DETR (Swin-L) pseudo-labeled the unlabeled RDD-2022 test set; student trained on GT + pseudo-labels. ## Metrics — RDD held-out validation (4,509 images, 11,470 boxes) | Class | mAP@50 | mAP@50-95 | Precision | Recall | F1 | |---|---:|---:|---:|---:|---:| | D00 Longitudinal | 0.590 | 0.324 | 0.675 | 0.520 | 0.588 | | D10 Transverse | 0.594 | 0.299 | 0.683 | 0.504 | 0.580 | | D20 Alligator | 0.687 | 0.375 | 0.713 | 0.617 | 0.662 | | D40 Pothole | 0.688 | 0.347 | 0.737 | 0.614 | 0.670 | | **Overall** | **0.640** | **0.337** | **0.702** | **0.564** | **0.625** | ## 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** (this model) | 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](https://huggingface.co/TamAko783/YOLO26s_RDD_FRDC_Distilled_v2) | YOLO26s | 9M | 2 teachers (Co-DETR+RTMDet) | 0.692 | 0.375 | 0....
Source context: 11 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo TamAko783/YOLO26n_RDD_FRDC_Distilled
| 0.337 |
| 0.702 |
| 0.564 |
| 0.625 |
| 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 |