Instructions to use hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation")# Load model directly from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation") model = MaskFormerForInstanceSegmentation.from_pretrained("hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e476c5d985f7d7583301b97bffb5575257c9a84b4eacc2c39e4030798d72aeca
- Size of remote file:
- 46.7 MB
- SHA256:
- 03831b94c4f9f3931111e1199e9edaea2db3fb2f10892f5ff9ec8a6c31333073
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