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:
- ff0c5977bcb0c6dba980167736106030f36c5cae719ad00d0def43e8139696e5
- Size of remote file:
- 45.8 MB
- SHA256:
- 0c015ff1221183c05e3fe8e613b5145fa44531c35300ab3f41bf7328e9162d1b
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