Instructions to use hf-tiny-model-private/tiny-random-ViTMSNForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ViTMSNForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/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-tiny-model-private/tiny-random-ViTMSNForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ViTMSNForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81a5c1575c7abd28541032a41b87375264f4f570629e3b18897023833c896894
- Size of remote file:
- 176 kB
- SHA256:
- 85e03dea7a86386b708185f5f256f28f8ef570a9860950f2eb662ed1fe21d810
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