Instructions to use hf-tiny-model-private/tiny-random-Mask2FormerForUniversalSegmentation 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-Mask2FormerForUniversalSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Mask2FormerForUniversalSegmentation") model = Mask2FormerForUniversalSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-Mask2FormerForUniversalSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ff3f72d39e3badd6406190ff8c12be9c5e041de8b918cca4c6903f00c98f10d
- Size of remote file:
- 47.3 MB
- SHA256:
- a9734af3d6f7873e188e54487849b688eb299530c808b7745ff59b6eda1b677c
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