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:
- ac393932bb803d37c04e8cacf433166b12e275cb4b3a0145a6a0c33cb86314ca
- Size of remote file:
- 343 MB
- SHA256:
- 849705ada47d195d78e04c39ab7ad4a1128108d523c76852f6473ff4ec8cd304
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