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:
- 885d2e11fdb6af4ac390e906ecb9c0a5da4fe95224da4d13ce182cd1dae45795
- Size of remote file:
- 293 kB
- SHA256:
- 8b33664acc22dc1a553818f413937e6edba71bde127bed8b19428be470d9500f
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