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:
- 62c38b79d7e2492d69c7a26787084d687ab96214fa280feef77a8061834b523b
- Size of remote file:
- 135 kB
- SHA256:
- cd5ddc97ea2c9f993d7391ef52b8c3704d8fd1f07b4517166520b6f9078a6498
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