Instructions to use HyeonCheol1205/basket_handle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use HyeonCheol1205/basket_handle with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("HyeonCheol1205/basket_handle") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Basket Handle YOLO11n
This is a YOLO11n object detection model trained to detect the black handle-like attachment on the front side of a tomato basket.
Model Details
- Model type: YOLO11n Object Detection
- Framework: Ultralytics
- Task: Object Detection
- Input: RGB image
- Output: Bounding box prediction for
basket_handle
Classes
| ID | Class |
|---|---|
| 0 | basket_handle |
Intended Use
This model is intended for object detection using an Intel RealSense camera and OpenManipulator-based robotic manipulation.
Example Usage
from ultralytics import YOLO
model = YOLO("basket_handle_yolo11n.pt")
results = model.predict(
source="test_image.jpg",
conf=0.5,
save=True
)
print(results)
Notes
Before using this model for robot manipulation, test it under different camera angles, lighting conditions, object distances, and backgrounds.
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