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
| from mmcv.utils import Registry, build_from_cfg | |
| PIXEL_SAMPLERS = Registry('pixel sampler') | |
| def build_pixel_sampler(cfg, **default_args): | |
| """Build pixel sampler for segmentation map.""" | |
| return build_from_cfg(cfg, PIXEL_SAMPLERS, default_args) | |