Instructions to use hf-internal-testing/tiny-random-DPTForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DPTForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, DPTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DPTForSemanticSegmentation") model = DPTForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-DPTForSemanticSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e90828eb4c1376736aa901f9cfd1b381dffded8cd74960c382d2b422464b36a2
- Size of remote file:
- 79.7 MB
- SHA256:
- 4cc5be3eb373dcd2785011fdf84c1cce2bac4d36728821d76b00233ed704e02b
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