Instructions to use ProDev9515/roadwork-72-RHTmE4s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProDev9515/roadwork-72-RHTmE4s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProDev9515/roadwork-72-RHTmE4s") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProDev9515/roadwork-72-RHTmE4s") model = AutoModelForImageClassification.from_pretrained("ProDev9515/roadwork-72-RHTmE4s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 217 Bytes
4df250a | 1 2 3 4 5 6 7 | {
"model_name": "roadwork-snapshot-RHTmE4s",
"description": "Snapshot model",
"version": "1.0.0",
"submitted_by": "5DDRHTmE4snSvrajS5JoayWdiosm1gAFVMJAE5BQ53RafEJN",
"submission_time": 1750439442
} |