Instructions to use keffy/hvc_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use keffy/hvc_model with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("keffy/hvc_model") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Rock vs Non-Rock YOLOv8 Model
This model was trained using YOLOv8 to classify rocks vs. non-rocks in an industrial setting. It is designed to aid in automated sorting processes by detecting non-rock objects in a conveyor belt system.
Model Details
- Framework: YOLOv8
- Training Data: Custom rock vs. non-rock dataset
- Purpose: Object detection to differentiate between rocks and non-rock objects
- License: Apache 2.0
Usage
To load the model:
from ultralytics import YOLO
# Load the model
model = YOLO('hvc_model/best.pt') # Update this path based on your Hugging Face model path
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Evaluation results
- mAP on Rock vs Non-Rock Datasetself-reported0.850