Instructions to use hf-tiny-model-private/tiny-random-Data2VecVisionForImageClassification 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-Data2VecVisionForImageClassification 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-Data2VecVisionForImageClassification") 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-Data2VecVisionForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3689af842dc4e7a24c43743ad00aa9280f88f6a96afc00910fe78a5f635143b0
- Size of remote file:
- 120 kB
- SHA256:
- c34ec248f461d2894c1ff0402c24cea4214557e14cf4ad1cd01d9e1075eaaac5
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