Instructions to use hf-tiny-model-private/tiny-random-LevitForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LevitForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-LevitForImageClassification") 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("hf-tiny-model-private/tiny-random-LevitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-LevitForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65f1d6fda4286b9e13820525a5856f0a383360d70bc6b33aef4591daa92c7c76
- Size of remote file:
- 28.2 MB
- SHA256:
- 43481c85f935ec5c7596aace147ef7cc45ab9572a7aee8896ecbdfb06b6958c9
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