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")# pip install -U transformers accelerate # 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
Download mmseg/apis/__init__.py from mccaly/test2: direct link, hf CLI and curl.
- Browser
- Download file 381 Bytes
-
https://huggingface.co/mccaly/test2/resolve/main/mmseg/apis/__init__.py
- Command line
-
hf download hf://mccaly/test2/mmseg/apis/__init__.py
-
curl -L -o __init__.py https://huggingface.co/mccaly/test2/resolve/main/mmseg/apis/__init__.py
381 Bytes
| from .inference import inference_segmentor, init_segmentor, show_result_pyplot | |
| from .test import multi_gpu_test, single_gpu_test | |
| from .train import get_root_logger, set_random_seed, train_segmentor | |
| __all__ = [ | |
| 'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor', | |
| 'inference_segmentor', 'multi_gpu_test', 'single_gpu_test', | |
| 'show_result_pyplot' | |
| ] | |