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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ce6505c1f090d9aad8007727d9cc7e98f0941fecc4bb1844820e891b41c86d5
- Size of remote file:
- 47.4 MB
- SHA256:
- 19334ea51897e9b79b5a44c059d549be80af0a486320940d84ecebb194a59863
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