Instructions to use 2nzi/Image_Surf_NotSurf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 2nzi/Image_Surf_NotSurf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="2nzi/Image_Surf_NotSurf") 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("2nzi/Image_Surf_NotSurf") model = AutoModelForImageClassification.from_pretrained("2nzi/Image_Surf_NotSurf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9232b03af01ff331bc25625e9c7509b4bfe18a45d3e4b31248cd2e601ff9a277
- Size of remote file:
- 344 MB
- SHA256:
- c69b3dbf5e36aa63c248131c95fd15fee40e8403ea8ee78e95bd33967041ff14
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