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:
- 3cdf4f344c5eab805d0e3d462b98b78b3fedf89592979cd7c41e8f01e3b60f26
- Size of remote file:
- 343 MB
- SHA256:
- 817c3d607ea5151b2ad2e15af3e6a22d7722e291b9203a691f2c0a65865d17ec
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