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:
- e333ee70a0dfb285eb487fa0905436c913226fd08b0e6c5fed688669b50fbad9
- Size of remote file:
- 177 kB
- SHA256:
- d35127b7f639f1bbe6f2049de23cf256e94b26d287c65dd059b65be6f4bb47c9
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