Cardd Yolov8s

CarDD YOLOv8s — Car Damage Detection & Segmentation

YOLOv8s fine-tuned on the CarDD dataset for vision-based car damage detection and instance segmentation. The model detects and segments six types of car damage and outperforms the original paper's best model (DCN+ ResNet-101) on every individual damage class.

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🗂️ Model Versions

Version	File	Task	mAP50
v1.0	v1.0/best.pt	Detection only	71.67%
v2.0	v2.0/best.pt	Detection + Segmentation	75.84% 🔥

v2.0 is the recommended version. It adds instance segmentation masks on top of detection and achieves significantly better results across all metrics.

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📈 v1.0 vs v2.0 — Side by Side Comparison

Overall Metrics

Metric	v1.0 (Detection)	v2.0 (Segmentation)	Improvement
mAP50 (Box)	71.67%	75.84%	✅ +4.17%
mAP50-95 (Box)	55.43%	58.93%	✅ +3.50%
Precision (Box)	75.11%	76.84%	✅ +1.73%
Recall (Box)	67.52%	68.27%	✅ +0.75%
Mask mAP50	❌ N/A	75.90%	✅ New
Mask mAP50-95	❌ N/A	57.00%	✅ New
Model size	22.5 MB	22.5 MB	➡️ Same
Inference speed	7.7ms	61ms	⚠️ Slower*

*Segmentation is slower due to mask generation on top of detection.

Per-Class Comparison (Box mAP50)

Class	v1.0	v2.0	Improvement
dent	57.8%	60.6%	✅ +2.8%
scratch	59.6%	63.2%	✅ +3.6%
crack	37.7%	55.5%	✅ +17.8%
glass_shatter	99.0%	97.9%	➡️ ~Same
lamp_broken	85.5%	86.8%	✅ +1.3%
tire_flat	90.5%	91.0%	✅ +0.5%

The biggest gain is on crack (+17.8%) — the hardest class in the dataset.

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📊 Full Results (v2.0)

Overall Performance (Test Set)

Metric	Box	Mask
[email protected]	75.84%	75.90%
[email protected]:0.95	58.93%	57.00%
Precision	76.84%	77.40%
Recall	68.27%	68.70%

Per-Class Results (v2.0 Test Set)

Class	Box mAP50	Mask mAP50
glass_shatter	97.9%	97.9%
tire_flat	91.0%	90.2%
lamp_broken	86.8%	88.2%
scratch	63.2%	60.4%
dent	60.6%	64.9%
crack	55.5%	54.0%

Benchmark Comparison vs CarDD Paper (v2.0)

Model	Backbone	mAP50
Mask R-CNN	ResNet-50	66.3%
Cascade Mask R-CNN	ResNet-50	64.7%
GCNet	ResNet-50	66.4%
HTC	ResNet-50	68.1%
DCN	ResNet-50	70.9%
Mask R-CNN	ResNet-101	67.7%
HTC	ResNet-101	68.4%
DCN	ResNet-101	69.8%
YOLOv8s-seg v2.0 (ours)	—	75.8% 🔥
DCN+	ResNet-50	77.4%
DCN+	ResNet-101	78.8%

Per-Class vs DCN+ ResNet-101 (Paper's Best)

Class	DCN+ ResNet-101	v2.0 (Box)	Gain
dent	40.5%	60.6%	✅ +20.1%
scratch	34.3%	63.2%	✅ +28.9%
crack	16.6%	55.5%	✅ +38.9%
glass_shatter	92.6%	97.9%	✅ +5.3%
lamp_broken	70.8%	86.8%	✅ +16.0%
tire_flat	86.0%	91.0%	✅ +5.0%

v2.0 beats DCN+ ResNet-101 on every individual damage class, while being ~10x smaller (22.5MB vs 200MB+) and requiring only a consumer GPU to train.

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📉 Training Curves (v2.0)

Training Curves

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🔢 Confusion Matrix (v2.0)

Confusion Matrix

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📈 PR & F1 Curves (v2.0)

Box PR Curve Box F1 Curve Mask PR Curve

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🖼️ Predictions (v2.0)

Validation Predictions

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📊 Dataset Label Distribution

Labels

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📉 Training Curves (v1.0)

Training Curves v1

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📊 Per-Class Comparison (v1.0)

Per-Class Comparison

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🖼️ Ground Truth vs Predictions (v1.0)

GT vs Pred

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🖼️ Predictions Grid (v1.0)

Predictions Grid

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🚀 Usage

Install Dependencies

pip install ultralytics huggingface_hub

Load v2.0 (Recommended)

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

model_path = hf_hub_download(
    repo_id="abdullahg7/cardd-yolov8s",
    filename="v2.0/best.pt"
)

model = YOLO(model_path)

results = model.predict(
    source="your_car_image.jpg",
    conf=0.25,
    iou=0.45
)

results[0].show()

Load v1.0 (Detection only)

model_path = hf_hub_download(
    repo_id="abdullahg7/cardd-yolov8s",
    filename="v1.0/best.pt"
)

model = YOLO(model_path)

Interpret Results (v2.0)

results = model.predict("car.jpg", conf=0.25)

# Detection boxes
for box in results[0].boxes:
    class_name = model.names[int(box.cls)]
    confidence = float(box.conf)
    print(f"Detected: {class_name} ({confidence:.2%})")

# Segmentation masks
if results[0].masks is not None:
    for mask in results[0].masks.data:
        print(f"Mask shape: {mask.shape}")  # (H, W) binary mask

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⚙️ Training Configuration (v2.0)

Parameter	Value
Base model	yolov8s-seg.pt
Epochs	50
Batch size	4
Image size	1024px
Optimizer	SGD
LR0	0.01
LRF	0.01
Momentum	0.937
Weight decay	0.0005
Warmup epochs	5.0
Cosine LR	True
Close mosaic	15

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🔴 Damage Categories

ID	Class	Description
0	dent	Surface deformation on car body
1	scratch	Linear paint damage
2	crack	Structural fractures
3	glass_shatter	Broken windows or windshields
4	lamp_broken	Damaged headlights or tail lights
5	tire_flat	Deflated or damaged tires

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📦 Dataset

• Name: CarDD (Car Damage Detection)
• Size: 4,000 high-resolution images, 9,000+ annotated instances
• Access: cardd-ustc.github.io (requires license agreement)
• Paper: Wang et al., IEEE T-ITS, 2023

Split	Images	Instances
Train	2,816	6,211
Val	810	1,744
Test	374	785

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⚠️ Limitations

• Lower performance on hard classes (dent, scratch, crack) due to small size and visual similarity
• Trained on exterior car damage only
• Best results on clear, well-lit images

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🔗 Links

• GitHub: abdullahghanem-a11y/car-damage-detection
• Dataset: cardd-ustc.github.io

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📚 Citation

@article{wang2023cardd,
  title     = {CarDD: A New Dataset for Vision-Based Car Damage Detection},
  author    = {Wang, Xinkuang and Li, Wenjing and Wu, Zhongcheng},
  journal   = {IEEE Transactions on Intelligent Transportation Systems},
  volume    = {24},
  number    = {7},
  pages     = {7202--7214},
  year      = {2023}
}

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📄 License

GNU AGPL-3.0 — in compliance with the YOLOv8 framework by Ultralytics.

⚠️ Dataset Notice: CarDD images are sourced from Flickr and Shutterstock. Proper licensing must be obtained before commercial use.