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:
- 580f7076524afe20564c1997c7394a7d9881c483ae0044ec52f61ff2744d463c
- Size of remote file:
- 176 kB
- SHA256:
- f4e771742a006661115a83b0efabc7c47cb1b09cb8b95efe33ed22ea0419abf2
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