| --- |
| license: mit |
| base_model: |
| - Ultralytics/YOLO11 |
| pipeline_tag: object-detection |
| tags: |
| - plant |
| - leaf |
| - leaves |
| --- |
| # YOLOv11 Model for Plant Leaves Detection |
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| This is a YOLOv11 model trained for detecting plant leaves. |
|
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| ## Model Details |
| - **Framework**: Ultralytics YOLO |
| - **Classes**: Leaf |
| - **Usage**: Designed for agriculture applications. |
|
|
| ## Dataset |
| Training dataset from https://www.kaggle.com/datasets/alexo98/leaf-detection |
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| Dataset adaptation to YOLO format from https://www.kaggle.com/code/luisolazo/leaf-detection-w-ultralytics-yolov8-and-tflite |
|
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| ## 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() |
| ``` |
|
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| ## Examples |
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