Instructions to use hf-internal-testing/tiny-random-DeiTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DeiTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-DeiTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-DeiTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3a1f36798e2308585e87b3009760a96253b593c7b76d00cfa324ecdbdcb50db
- Size of remote file:
- 196 kB
- SHA256:
- 2133f07c68098d400a850e2df537bb0cbc4eeeb33e33f5b5b360ee9eced4dfdf
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