Instructions to use universalml/cds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/cds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/cds") 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/cds") model = AutoModelForImageClassification.from_pretrained("universalml/cds", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f5f76c5dd6c87a7792d6c68e1047a97eb619bb7b0fc83e88069829831a48571
- Size of remote file:
- 343 MB
- SHA256:
- 6d8f8d3207bf26345f05b25b20d97228b06cafc4d25a28f5edc0b590bb0b625a
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