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:
- a471087362bfa3e420eac8b677500df7658a6f8b8d0dbd815b81b7f622bffe5e
- Size of remote file:
- 4.09 kB
- SHA256:
- a354c1c5247a2b72d468989d5f9e2b2eb7c6bcf44954f9c083e63ab22f564991
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