Instructions to use hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation 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-OneFormerForUniversalSegmentation with Transformers:
# Load model directly from transformers import AutoProcessor, OneFormerForUniversalSegmentation processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation") model = OneFormerForUniversalSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25dfb06a22301414004747fc1cbba59124ae58f7472641a483a338b74bd10397
- Size of remote file:
- 47.9 MB
- SHA256:
- 4fadda1f1d94a9e55734ddf2efabe12844bfddd5d9abcad1c38566cc5769af88
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