Instructions to use muse0515/roadwork-72-RHTmE4s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muse0515/roadwork-72-RHTmE4s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="muse0515/roadwork-72-RHTmE4s") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("muse0515/roadwork-72-RHTmE4s") model = AutoModelForImageClassification.from_pretrained("muse0515/roadwork-72-RHTmE4s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_card.json from muse0515/roadwork-72-RHTmE4s: direct link, hf CLI and curl.
- Browser
- Download file 217 Bytes
-
https://huggingface.co/muse0515/roadwork-72-RHTmE4s/resolve/main/model_card.json
- Command line
-
hf download hf://muse0515/roadwork-72-RHTmE4s/model_card.json
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curl -L -o model_card.json https://huggingface.co/muse0515/roadwork-72-RHTmE4s/resolve/main/model_card.json
217 Bytes
| { | |
| "model_name": "roadwork-snapshot-RHTmE4s", | |
| "description": "Snapshot model", | |
| "version": "1.0.0", | |
| "submitted_by": "5DDRHTmE4snSvrajS5JoayWdiosm1gAFVMJAE5BQ53RafEJN", | |
| "submission_time": 1750439442 | |
| } |