YOLO11s Strawberry Detection Model
This is a YOLO11s object detection model trained to identify and classify strawberries into two categories: Ripe and Unripe. It is optimized for real-time inference and high accuracy.
Model Details
- Model Type: YOLO11s (Ultralytics)
- Task: Object Detection
- Classes: 2 (Ripe, Unripe)
- Training Dataset: 5,000 images
Performance Metrics
The model was evaluated on the validation set with the following results:
| Category | Precision | Recall | mAP@50 | mAP@50-95 |
|---|---|---|---|---|
| All | 0.853 | 0.877 | 0.930 | 0.775 |
| Ripe | 0.865 | 0.881 | 0.928 | 0.752 |
| Unripe | 0.841 | 0.872 | 0.932 | 0.798 |
- Inference Speed: ~25.5ms per image (making it highly suitable for real-time video processing).
How to Use
You can load this model directly using the Ultralytics library:
from ultralytics import YOLO
# Load the model
model = YOLO('path/to/downloaded/model.pt')
# Run inference on an image
results = model('path/to/your/strawberry_image.jpg')
results[0].show()