Instructions to use hf-tiny-model-private/tiny-random-MobileViTForImageClassification 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-MobileViTForImageClassification 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-MobileViTForImageClassification") 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-MobileViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-MobileViTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cf1d74d7e1891f6df5b536cd9db6ba03f18a9437f88e4ca77120c1fd3badb57
- Size of remote file:
- 19.9 MB
- SHA256:
- 005bed6ba3b2ba5c50a96210aa9d14e13e9d3a23bbf6596a7e40fa1ad8ef79a5
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