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
File size: 427 Bytes
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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
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