Instructions to use hf-internal-testing/tiny-random-SegformerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SegformerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-SegformerModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-SegformerModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SegformerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cd46ce7e1ee582e9ef4584d55b4bb01cd3b856c955f96f7d626ef4af238be56
- Size of remote file:
- 3.05 MB
- SHA256:
- dac07f7a5ae1d8b079c88b0701afeb25b25c6eb07fbcd715cb11344ab0851dab
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