Instructions to use hf-tiny-model-private/tiny-random-SegformerModel 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-SegformerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-SegformerModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-SegformerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-SegformerModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e255acce629102052f0f571d488fc47747443ea3d9f05e4e420a46fb5bad0ab1
- Size of remote file:
- 3.01 MB
- SHA256:
- 1e5e7e5008d7e8e49e3dd8ab1213aced8479e6af4ea6007306347761b8caa651
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