Instructions to use universalml/what with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/what with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/what") 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("universalml/what") model = AutoModelForImageClassification.from_pretrained("universalml/what", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89762e5ec2cf5cf1536a1775ae2b7d751bbb8d55813f81b7a8ffd8f86938bfc8
- Size of remote file:
- 4.09 kB
- SHA256:
- 8db6b9aa83a08fb245f275f5fb67094adef76b1282d9c057759cce93f967c3ad
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