Instructions to use hf-internal-testing/tiny-random-ViTMSNForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTMSNForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ViTMSNForImageClassification") 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-ViTMSNForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ViTMSNForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 075064ef1f35ebddb771311eba2ceac1e8db03c11f3d19d26d9887ab4fe99bbd
- Size of remote file:
- 195 kB
- SHA256:
- b94dfad4e2ec26efbe735375708bbda440c77beb1e2d4c5ff936389a8d055414
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