Instructions to use universalml/lksef with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/lksef with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/lksef") 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/lksef") model = AutoModelForImageClassification.from_pretrained("universalml/lksef", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4532c64a548517823457929f1504ac79f331c8a4fa2206290257c582b0d2a990
- Size of remote file:
- 4.09 kB
- SHA256:
- b501e7722448cb3c0de77f69b835a35cdba1d9c861885a882074c43f3d169698
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