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
| [yapf] | |
| based_on_style = pep8 | |
| blank_line_before_nested_class_or_def = true | |
| split_before_expression_after_opening_paren = true | |
| [isort] | |
| line_length = 79 | |
| multi_line_output = 0 | |
| known_standard_library = setuptools | |
| known_first_party = mmseg | |
| known_third_party = PIL,cityscapesscripts,cv2,detail,matplotlib,mmcv,numpy,onnxruntime,oss2,pytest,scipy,terminaltables,torch | |
| no_lines_before = STDLIB,LOCALFOLDER | |
| default_section = THIRDPARTY | |