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:
- 71a34e1067d07b3608e35d93b685cbeddfe7c54d4945a1fea4887b0defabb6da
- Size of remote file:
- 196 kB
- SHA256:
- 9dd89ecb84e784053a9a685a2fd8d1a0bcaad7275b2fe9fe039860bf0a2b37b6
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