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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b251ac8b767194e05df5c6d937a4f36ee91f9272572f88adae89ac55112a09cc
- Size of remote file:
- 195 kB
- SHA256:
- 7a9f164f06b411c4afeb83b9a0baa9d9dbd40a30eb8446eedfd2d3e8f5db7d40
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