Instructions to use manudeva/intervUone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manudeva/intervUone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="manudeva/intervUone") 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("manudeva/intervUone") model = AutoModelForImageClassification.from_pretrained("manudeva/intervUone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f3091984741f081d1f328f8b53335f3ae2658bf44813e96f6bd7322986fa65b
- Size of remote file:
- 343 MB
- SHA256:
- 12865af49de8cabbdb5f45e147e7c7425dc0e66401fa1af26f2c69c099b402c4
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