Instructions to use universalml/df with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/df with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/df") 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/df") model = AutoModelForImageClassification.from_pretrained("universalml/df", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c67f9ede92b99d24f4f3b10d8dd8abb3631aaf17c22a92d2e424b53ed501c60c
- Size of remote file:
- 4.09 kB
- SHA256:
- 0496876c28ebe2277090970e978d04ee54e4022497e2d2a5f2a651b224052db2
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