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
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addopts = --xdoctest --xdoctest-style=auto
norecursedirs = .git ignore build __pycache__ data docker docs .eggs
filterwarnings= default
ignore:.*No cfgstr given in Cacher constructor or call.*:Warning
ignore:.*Define the __nice__ method for.*:Warning
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