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:
- d246f4ce9ac4adf5e07b83c931830f564414fcdfd1dc1d8e9beb5eef5d417478
- Size of remote file:
- 991 kB
- SHA256:
- c00ea7cf48a9c1651a05d9b2a2570d029e76110ab498903d24c6c7065d64b648
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