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:
- 0c93031cbb54e8b769fa3cefa1000183256d343dda4b6d595fe8502599d105d6
- Size of remote file:
- 221 kB
- SHA256:
- 14a3b96d156d346bab5d2df80eb57ef730e3e99ddd5f616d8034d3148dc96dea
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