Instructions to use hf-internal-testing/tiny-random-BeitForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BeitForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, BeitForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-BeitForSemanticSegmentation") model = BeitForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-BeitForSemanticSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e12070827167d83ae107f9ebda22e8ecfb0b5807fa43c99ce39557c5c8aad6d
- Size of remote file:
- 956 kB
- SHA256:
- 2be1fcc317767f30a62810422d736f4d6c256419a3ae3aa9fed66d955fee4a7b
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