Instructions to use hf-internal-testing/tiny-random-ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ViTForImageClassification") 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-ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ViTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25c7257bc52e00e1e714e0b2f1d907500d5e23ab9019e86157daff3432e772f0
- Size of remote file:
- 176 kB
- SHA256:
- 0d1195c81173cc9130c6ba8e33e05b0c8ef23b1b5cbaa2e0e49e5c1be42f0b27
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