---
license: mit
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
- plant
- leaf
- leaves
---
# YOLOv11 Model for Plant Leaves Detection

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f9f9fcca8fdc72b5d3b854/ez9_FEOoq8Zt_VkNmBRhB.png)

This is a YOLOv11 model trained for detecting plant leaves.

## Model Details
- **Framework**: Ultralytics YOLO
- **Classes**: Leaf
- **Usage**: Designed for agriculture applications.

## Dataset
Training dataset from https://www.kaggle.com/datasets/alexo98/leaf-detection

Dataset adaptation to YOLO format from https://www.kaggle.com/code/luisolazo/leaf-detection-w-ultralytics-yolov8-and-tflite

## Usage

```python
from ultralytics import YOLO
import cv2
import matplotlib.pyplot as plt

# Load the YOLO model
model = YOLO('yolo11x_leaf.pt')

# Run inference on an image or directory
result = model.predict('file/directory', task="detect", save=False, conf=0.15)

# Load the original image
image_path = result.path
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Annotate the image with predictions
annotated_image = result.plot()

# Display the annotated image
plt.figure(figsize=(10, 7))
plt.imshow(annotated_image)
plt.axis("off")
plt.title(f"Predictions for Image")
plt.show()
```

## Examples


![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f9f9fcca8fdc72b5d3b854/8vQB1rwEaMVU9O5u0kUdh.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f9f9fcca8fdc72b5d3b854/4_NqnutjDxmXu2G0c4bWd.png)