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