Instructions to use hf-internal-testing/tiny-random-UperNetForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UperNetForSemanticSegmentation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-UperNetForSemanticSegmentation") model = UperNetForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-UperNetForSemanticSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-UperNetForSemanticSegmentation: direct link, hf CLI and curl.
- Browser
- Download file 86.9 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-UperNetForSemanticSegmentation/resolve/f1871cb388bc0b203f5397bfc06a373736c2fb9c/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-UperNetForSemanticSegmentation@f1871cb388bc0b203f5397bfc06a373736c2fb9c/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-UperNetForSemanticSegmentation/resolve/f1871cb388bc0b203f5397bfc06a373736c2fb9c/pytorch_model.bin
86.9 MB
- Xet hash:
- e11ca0d9f35a89bc9843409ce3a6f9d9b85108cdaed7955eb46f8dc1081f435a
- Size of remote file:
- 86.9 MB
- SHA256:
- 6ea199d10f93b8c8abac2faf11fca3315dd568c5d67a1355e0721a7daf776554
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