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:
- 2736814a345f5dc06cac9adefba39025f4c3a4921a9c3feb41c2eb3dc23ea13b
- Size of remote file:
- 45.7 MB
- SHA256:
- dacd21588a01290a63cd2505246c958f2b87f9c174c3d386972b0ef0f8c41ee7
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