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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89c7b7c18dfc6273cf9405b576eba085100e65a85b6d05896f6a761e65f40898
- Size of remote file:
- 79.7 MB
- SHA256:
- 52f515b8bb4268d7baa7111f744be6ba687235120717fbfdcc631d2ed5e30c3c
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