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:
- 6723430fca77255fdc7ad6a3b840c2973360693be5503204f24d96bb1bdf2c7e
- Size of remote file:
- 3.05 MB
- SHA256:
- 40251f9deb50623f515503442806dc4786eb1bfc36eed7949114b112dd2e69c4
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