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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8edc6d452323e9e0595947b49200481087f84da7ceea8e4ff120e27eee5718a3
- Size of remote file:
- 3.23 MB
- SHA256:
- 0d7d3b40c5d9b727fdfb33a7c9425570390547c43651a722a773403bc8e4f3ed
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