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:
- 29860a8bd527d245cd0346a4aa8c18a27df490c3931c7a0c0e9c51bd396a882b
- Size of remote file:
- 28.3 MB
- SHA256:
- f751a228aa815eb296fa46e2c06ff77956f09335e040ac92c501e02dc5c6392f
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