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:
- 6e695b478cab25df8f116a2319f60afc760ff2ca2401078530d2abbe382f4255
- Size of remote file:
- 992 kB
- SHA256:
- 96e35e70e07565b74fc03ac65800e00596ddc92041e5b2204259bc6b0ecf878b
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