Instructions to use Deval1004/streetvision13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deval1004/streetvision13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Deval1004/streetvision13") 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("Deval1004/streetvision13") model = AutoModelForImageClassification.from_pretrained("Deval1004/streetvision13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_card.json from Deval1004/streetvision13: direct link, hf CLI and curl.
- Browser
- Download file 241 Bytes
-
https://huggingface.co/Deval1004/streetvision13/resolve/main/model_card.json
- Command line
-
hf download hf://Deval1004/streetvision13/model_card.json
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curl -L -o model_card.json https://huggingface.co/Deval1004/streetvision13/resolve/main/model_card.json
241 Bytes
| { | |
| "model_name": "natix-network-org", | |
| "description": "Snapshot model from natix-network-org/roadwork", | |
| "version": "1.0.0", | |
| "submitted_by": "5HHYYzx9tdercvprydaUk29X4xSVKdpxtjU9m44jLBjCYhYw", | |
| "submission_time": 1749097853 | |
| } |