Instructions to use hf-tiny-model-private/tiny-random-DeiTForImageClassificationWithTeacher 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-DeiTForImageClassificationWithTeacher 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-DeiTForImageClassificationWithTeacher") 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-DeiTForImageClassificationWithTeacher") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-DeiTForImageClassificationWithTeacher") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b052e42287811d5459649a4aa50e65638e1d591158b7cb27ea0c1aae60633a75
- Size of remote file:
- 197 kB
- SHA256:
- 7f717e6503829d475cd320c5b49385a4429f0fdf94898fb9feebcc0f9e035ba3
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