Instructions to use hf-tiny-model-private/tiny-random-BeitForImageClassification 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-BeitForImageClassification 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-BeitForImageClassification") 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-BeitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-BeitForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca04ee3961d056cf80ae1775242d5850e9cbaa1169a429a4af8e69a377dbe531
- Size of remote file:
- 135 kB
- SHA256:
- 7e92a95430b8f85aa95c8ea19f02966c8f3e979e7edaf02f39786913e2299af2
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