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:
- b1abf9d16b79a6663bdb8afef28b00c443d87bb66bf3e5a8edd30093951b1250
- Size of remote file:
- 957 kB
- SHA256:
- af09ddfac00c278f7ae86cb01f7e43aacff7cd4c47d35a2df27c3d83237928aa
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