---
license: apache-2.0
base_model:
- Ultralytics/YOLO11
language:
- en
- nl
tags:
- object-detection
- segmentation
- vision
- ultralytics
- yolo11
- pytorch
- pickle
- roboflow-universe
library_name: ultralytics
---

# Yolo11n bonnetjes
A YOLO11n segmentation model trained on receipts dataset.

## Model summary
- Layers: 203 layers
- Parameters: 2,842,803
- GFLOPs: 10.4
- File size: 6 MB

![example](./example.png)

## Requirements
```bash
pip install ultralytics
```

## Python
```python
from ultralytics import YOLO

# Load model
model = YOLO("yolo11n-seg-bonnetjes.pt")

# Load image
image = Image.open('image.jpg')

# Inference
results = model.predict(
    image,
    imgsz=640,
    conf=0.60,
)

# Display result
results[0].show()
```

## Dataset
- Train: 4428
- Valid: 242
- Test: 146

## Preprocessing (created w/ Roboflow)
- Auto-Orient: Applied
- Resize: 640x640

## Augmentations (created w/ Roboflow)
- Outputs per training example: 3
- Flip: Horizontal
- 90° Rotate: Clockwise, Counter-Clockwise, Upside Down
- Crop: 0% Minimum Zoom, 20% Maximum Zoom
- Rotation: Between -15° and +15°
- Shear: ±10° Horizontal, ±10° Vertical
- Grayscale: Apply to 15% of images
- Saturation: Between -27% and +27%
- Brightness: Between -21% and +21%
- Exposure: Between -82% and +82%
- Noise: Up to 0.1% of pixels

## Results
![example](./results.png)