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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 721c90571fc0cf1b8708fe27b9179af324e3656da274fe974b5f2317e40b6729
- Size of remote file:
- 976 MB
- SHA256:
- b67e032728959023a14b7a6a4831009367d58331cd2d188fa6540e7e40a05121
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