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:
- 41fb5244283c1e91efe07bbda76ea94bef8de2a8ef1233e8ea82aa7823586197
- Size of remote file:
- 47.8 MB
- SHA256:
- 3084acec33500a6fc58d7b00c95f1eb826172c001ecd3729dd2ada5dbcd187d4
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