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yolov11n-car-damage – AI Model by vineetsarpal | AlphaNeural AI

You can deploy this model and start earning money today!

vineetsarpal/
yolov11n-car-damage

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computer-vision

object-detection

Ultralytics/YOLO11

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YOLOv11-Nano Car Damage Detection

A lightweight YOLOv11-Nano model fine-tuned for automated detection and classification of automobile damage across 14 damage categories.

⚙️ Model Details

Architecture: YOLOv11-Nano
Framework: Ultralytics
Input Size: 640x640
Format: PyTorch (.pt)

📊 Model Performance

mAP@0.5: 79.7%
mAP@0.5:0.95: 55.2%
Model Size: ~6MB (Nano variant)
Speed: Real-time inference capable

🎯 Use Cases

Insurance claim automation
Vehicle inspection systems
Auto repair assessment
Fleet damage monitoring

📁 Dataset

Trained on the Automobile Damage Detection Dataset from Roboflow Universe.

Images: ~6,900 annotated images
Classes: 14 damage categories
Annotations: Bounding boxes

Damage Categories

Front-windscreen-damage
Headlight-damage
Rear-windscreen-Damage
Runningboard-Damage
Sidemirror-Damage
Taillight-Damage
bonnet-dent
boot-dent
doorouter-dent
fender-dent
front-bumper-dent
quaterpanel-dent
rear-bumper-dent
roof-dent

📈 Training Metrics

Results Plot

Results Plot

Confusion Matrix

Confusion Matrix

🚀 Usage

python

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="vineetsarpal/yolov11n-car-damage", 
    filename="best.pt"
)
model = YOLO(model_path)

results = model.predict("/path/to/your/image.jpg", save=True)

⚠️ Limitations

Class imbalance: Reduced accuracy on boot-dent detection due to limited training examples
Image quality: Performance degrades on low-resolution (<640px), poorly-lit, or low-contrast images