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:
- 0141f2f153d9f7d69ec8569f2117a2f0f12cdae2a8fb56c85ed57f4d5000b03b
- Size of remote file:
- 119 kB
- SHA256:
- 6e2133a501e284403fae0c6ac96fbc364d424b433196a8b12959fe9fc74ec475
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