Instructions to use hf-tiny-model-private/tiny-random-DPTForSemanticSegmentation 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-DPTForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, DPTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-DPTForSemanticSegmentation") model = DPTForSemanticSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-DPTForSemanticSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfc256437115f2978776e0883c7106264d15eddf53194ef457246b802c3f890a
- Size of remote file:
- 79.7 MB
- SHA256:
- 8535345448f385a7be0b844e27e1b5a275da945b1eddf49c29f71dc7857c7bb9
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