Instructions to use hf-internal-testing/tiny-random-Data2VecVisionForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecVisionForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-Data2VecVisionForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-Data2VecVisionForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd8aac6dc106b49c9c08fb7061c0159123bb0f824ca4a0d812125d0d625b9f83
- Size of remote file:
- 220 kB
- SHA256:
- d52d4da1f517a354afa21dbf74ab7f91832081104f415b0d7b8ae4b641877a8d
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