# Install YOLOv12 ultralytics fork
# pip install git+https://github.com/sunsmarterjie/yolov12.git

from ultralytics import YOLO

# Load model
model = YOLO("rezzzq/yolo12s-road-damage-rdd2022")
# or download and load locally:
# model = YOLO("yolo12s_RDD2022_best.pt")

# Run inference
results = model("path/to/road_image.jpg")

# Process results
for result in results:
    boxes = result.boxes
    for box in boxes:
        cls = int(box.cls[0])
        conf = float(box.conf[0])
        print(f"Class: {model.names[cls]}, Confidence: {conf:.2%}")

Training Details

Architecture: YOLOv12-small (with A2C2f attention modules)
Input Size: 640x640
Classes: 5 (D00, D10, D20, D40, Repair)
Framework: Ultralytics (YOLOv12 fork)

Base Model: YOLOv12s pretrained
Dataset: RDD2022 (Road Damage Dataset)
Image Size: 640x640
Batch Size: 32

Citation

If you use this model, please cite the RDD2022 dataset:

@article{arya2022rdd2022,
  title={RDD2022: A multi-national image dataset for automatic Road Damage Detection},
  author={Arya, Deeksha and others},
  year={2022}
}