Instructions to use hf-internal-testing/tiny-random-MobileViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-MobileViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-MobileViTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 569c795898e0575de778f0cbd4f4ee839dfb84f72e8a3e6b12df4bcfb6fb9826
- Size of remote file:
- 19.9 MB
- SHA256:
- b9015ed95315369f40b23d78b96ce7ca75cd30c2e6dcf978750c104166d17760
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