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 configs/_base_/default_runtime.py from mccaly/test2: direct link, hf CLI and curl.
- Browser
- Download file 321 Bytes
-
https://huggingface.co/mccaly/test2/resolve/refs%2Fpr%2F2/configs/_base_/default_runtime.py
- Command line
-
hf download hf://mccaly/test2@refs/pr/2/configs/_base_/default_runtime.py
-
curl -L -o default_runtime.py https://huggingface.co/mccaly/test2/resolve/refs%2Fpr%2F2/configs/_base_/default_runtime.py
321 Bytes
| # yapf:disable | |
| log_config = dict( | |
| interval=50, | |
| hooks=[ | |
| dict(type='TextLoggerHook', by_epoch=False), | |
| # dict(type='TensorboardLoggerHook') | |
| ]) | |
| # yapf:enable | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| cudnn_benchmark = True | |