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
| [pytest] | |
| 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 | |