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:
- 1ead5f55f67f8eb255a712c3732e01ffb8cbe02cbef34408843ba4b47aca995a
- Size of remote file:
- 297 kB
- SHA256:
- b0f0de969c657e64fa8d082a499f7e8be155005a7d247e22f80993ea383b0dc8
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