Instructions to use hf-tiny-model-private/tiny-random-DeiTForImageClassification 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-DeiTForImageClassification 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-DeiTForImageClassification") 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-DeiTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-DeiTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3143ded4bc94f035c6cbc643133796d9410b02d1431557f394b3ea6c5be02c6d
- Size of remote file:
- 293 kB
- SHA256:
- 2e7577203e6cd5b20dc22e0b35a451241622706f1a623146db8c277368063e87
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