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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f16d6bc68c743ad15040fcd7b0ff05e99b8a0a3f94dbd3488a14b4fe5766e63
- Size of remote file:
- 136 kB
- SHA256:
- b4f051a7adf3c7eb82919a53ad751340929fc6247e762ac7e3a64e9cdbf697b1
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