Instructions to use hf-internal-testing/tiny-random-SegformerForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SegformerForSemanticSegmentation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-SegformerForSemanticSegmentation") model = SegformerForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-SegformerForSemanticSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-SegformerForSemanticSegmentation: direct link, hf CLI and curl.
- Browser
- Download file 4.32 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-SegformerForSemanticSegmentation/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-SegformerForSemanticSegmentation@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-SegformerForSemanticSegmentation/resolve/refs%2Fpr%2F1/model.safetensors
4.32 MB
- Xet hash:
- 34e5cc1109b6c9a9fa8fa2ba34b301fdc5d64e7b1f6051fd40a35c9acb5a96f3
- Size of remote file:
- 4.32 MB
- SHA256:
- eaf5bd43102d38058a6a7a5b9f0b3a712fe894b4944a4ba775ba0e820e011930
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