Instructions to use hf-tiny-model-private/tiny-random-Data2VecVisionForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Data2VecVisionForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, Data2VecVisionForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionForSemanticSegmentation") model = Data2VecVisionForSemanticSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionForSemanticSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0b54d7926db62acbc09875c6f49e710375466ba43e04bdb3cbea7f904ca6cfb
- Size of remote file:
- 992 kB
- SHA256:
- da5c430f0b623112022baa714cebff94f5c6fe0a69aa2a48a0d0782616d8c241
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