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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfa46512a2fff0b59649cd2f459bb6b89925573d89ae171e6501329f8e239318
- Size of remote file:
- 79.7 MB
- SHA256:
- 5c32d6d28d9550264657700c8a18846bf7f0743ebca10db6183e0332982848b3
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