Upload YOLOv11 car damage segmentation model

README.md

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+# Car Damage Segmentation YOLOv11 Model
+
+## Model Overview
+This model is a fine-tuned YOLOv11-seg specifically optimized for vehicle damage segmentation. It can accurately detect and segment various types of vehicle damage using the CarDD dataset.
+
+## Model Details
+- **Base Architecture**: YOLOv11-seg
+- **Training Dataset**: CarDD dataset (Car Damage Detection)
+- **Input Resolution**: 1280×1280
+- **Damage Classes**:
+  - crack
+  - dent
+  - glass shatter
+  - lamp broken
+  - scratch
+  - tire flat
+
+## Performance Metrics
+
+### Overall Performance
+| Task | Precision | Recall | mAP50 | mAP50-95 |
+|------|-----------|--------|-------|----------|
+| Box  | 0.753     | 0.689  | 0.734 | 0.513    |
+| Mask | 0.762     | 0.692  | 0.735 | 0.503    |
+
+### Class-Specific Performance (Box Detection)
+| Class         | Precision | Recall | mAP50 | mAP50-95 |
+|---------------|-----------|--------|-------|----------|
+| crack         | 0.649     | 0.362  | 0.452 | 0.249    |
+| dent          | 0.607     | 0.524  | 0.565 | 0.324    |
+| glass shatter | 0.879     | 1.000  | 0.986 | 0.656    |
+| lamp broken   | 0.749     | 0.816  | 0.838 | 0.639    |
+| scratch       | 0.673     | 0.563  | 0.595 | 0.327    |
+| tire flat     | 0.958     | 0.871  | 0.971 | 0.880    |
+
+### Class-Specific Performance (Mask Segmentation)
+| Class         | Precision | Recall | mAP50 | mAP50-95 |
+|---------------|-----------|--------|-------|----------|
+| crack         | 0.665     | 0.368  | 0.445 | 0.167    |
+| dent          | 0.626     | 0.530  | 0.562 | 0.281    |
+| glass shatter | 0.881     | 1.000  | 0.986 | 0.724    |
+| lamp broken   | 0.759     | 0.825  | 0.857 | 0.669    |
+| scratch       | 0.684     | 0.560  | 0.589 | 0.284    |
+| tire flat     | 0.958     | 0.869  | 0.971 | 0.891    |
+
+### Dataset Stats
+- **Images**: 571
+- **Instances**: 1247
+- **Instance Distribution**:
+  - crack: 152
+  - dent: 366
+  - glass shatter: 91
+  - lamp broken: 103
+  - scratch: 482
+  - tire flat: 53
+
+## Training Configuration
+```yaml
+batch: 24
+imgsz: 1280
+epochs: 300
+patience: 15
+workers: 16
+device: cuda
+cache: True
+amp: True
+overlap_mask: True
+warmup_epochs: 3
+mosaic: 1.0
+mixup: 0.1
+copy_paste: 0.1
+```
+
+## Usage
+
+### Installation
+```bash
+pip install ultralytics
+```
+
+### Inference with Python
+```python
+from ultralytics import YOLO
+
+# Load the model
+model = YOLO('best.pt')
+
+# Run inference on an image
+results = model('path/to/car/image.jpg')
+
+# Process results
+for result in results:
+    boxes = result.boxes  # Bounding boxes
+    masks = result.masks  # Segmentation masks
+```
+
+## Model Strengths
+- Excellent performance on glass shatter (98.6% mAP50) and tire flat (97.1% mAP50)
+- Good balance between precision and recall for most damage types
+- High-quality segmentation masks, particularly for well-defined damage types
+
+## Model Limitations
+- Lower recall for crack detection (36.2%)
+- Performance varies across damage types (mAP50 ranges from 45.2% for cracks to 98.6% for glass shatter)
+- Small damages might be missed, particularly fine cracks
+- Works best with clear, well-lit images of vehicles
+
+## License
+This model is available under the [insert license type].

best.pt

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+size 20682724