Instructions to use mccaly/test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mccaly/test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mccaly/test2")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("mccaly/test2") model = UperNetForSemanticSegmentation.from_pretrained("mccaly/test2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9998f955a4711a4cc9c9fbba82bad4abe38fa2d246b06c9539eca81e770a8914
- Size of remote file:
- 326 MB
- SHA256:
- c81064aa324ba40024bd4696c3940b76befe5525dc80d2de99ab703160bcf0a3
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