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:
- 7638ed57d074f3e2eb88b97cfc3a1cfe17dac0629a9f59a141b9aa9c019c6aab
- Size of remote file:
- 136 kB
- SHA256:
- 8498aa422c4e6768515e9707314f9455ddc747ded2b4df173f1980e757e66e83
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