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 huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("keffy/hvc_model", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download config.json from keffy/hvc_model: direct link, hf CLI and curl.
- Browser
- Download file 324 Bytes
-
https://huggingface.co/keffy/hvc_model/resolve/main/config.json
- Command line
-
hf download hf://keffy/hvc_model/config.json
-
curl -L -o config.json https://huggingface.co/keffy/hvc_model/resolve/main/config.json
324 Bytes
| { | |
| "model_name": "Rock vs Non-Rock YOLOv8 Model", | |
| "framework": "ultralytics", | |
| "task": "object-detection", | |
| "weights": "best.pt", | |
| "config_file": "config.yaml", | |
| "id2label": { | |
| "0": "rock", | |
| "1": "non-rock" | |
| }, | |
| "description": "Detects rock and non-rock objects.", | |
| "license": "MIT", | |
| "author": "HVC TRU" | |
| } | |