Instructions to use hf-internal-testing/tiny-random-Data2VecVisionForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecVisionForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, Data2VecVisionForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Data2VecVisionForSemanticSegmentation") model = Data2VecVisionForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-Data2VecVisionForSemanticSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45144842a966794337f5076d9565efa42ff5cd8f7e0c67f50245f06022c6191d
- Size of remote file:
- 1.14 MB
- SHA256:
- f8ccb1ebb1fd068ad1e09d6d6706db8ae4c7a3b53406356a1c9341a827034fda
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