Instructions to use hf-internal-testing/tiny-random-BeitForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BeitForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-BeitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-BeitForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b40066f29706b0c61aba2ca30d313a015c97b53a07c86a43bbc53aad51aa0a13
- Size of remote file:
- 119 kB
- SHA256:
- 822089b2c3d6d9b92a9b55a430c4e85ce5ef098cd224fd117174901b8657949e
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