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:
- 9a5432d3452babd998597ad22ca1259347a918cda3d9871af9d9629d0580943a
- Size of remote file:
- 3.23 MB
- SHA256:
- ec548c2d1d2a78a46001bc1aeae8750b418a8ec9c104de1239f38716ef37088a
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