Instructions to use toto10/Ooga with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toto10/Ooga with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("toto10/Ooga", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download extensions/microsoftexcel-controlnet/annotator/mmpkg/mmseg/models/builder.py from toto10/Ooga: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/toto10/Ooga/resolve/main/extensions/microsoftexcel-controlnet/annotator/mmpkg/mmseg/models/builder.py
- Command line
-
hf download hf://toto10/Ooga/extensions/microsoftexcel-controlnet/annotator/mmpkg/mmseg/models/builder.py
-
curl -L -o builder.py https://huggingface.co/toto10/Ooga/resolve/main/extensions/microsoftexcel-controlnet/annotator/mmpkg/mmseg/models/builder.py
1.2 kB
| import warnings | |
| from annotator.mmpkg.mmcv.cnn import MODELS as MMCV_MODELS | |
| from annotator.mmpkg.mmcv.utils import Registry | |
| MODELS = Registry('models', parent=MMCV_MODELS) | |
| BACKBONES = MODELS | |
| NECKS = MODELS | |
| HEADS = MODELS | |
| LOSSES = MODELS | |
| SEGMENTORS = MODELS | |
| def build_backbone(cfg): | |
| """Build backbone.""" | |
| return BACKBONES.build(cfg) | |
| def build_neck(cfg): | |
| """Build neck.""" | |
| return NECKS.build(cfg) | |
| def build_head(cfg): | |
| """Build head.""" | |
| return HEADS.build(cfg) | |
| def build_loss(cfg): | |
| """Build loss.""" | |
| return LOSSES.build(cfg) | |
| def build_segmentor(cfg, train_cfg=None, test_cfg=None): | |
| """Build segmentor.""" | |
| if train_cfg is not None or test_cfg is not None: | |
| warnings.warn( | |
| 'train_cfg and test_cfg is deprecated, ' | |
| 'please specify them in model', UserWarning) | |
| assert cfg.get('train_cfg') is None or train_cfg is None, \ | |
| 'train_cfg specified in both outer field and model field ' | |
| assert cfg.get('test_cfg') is None or test_cfg is None, \ | |
| 'test_cfg specified in both outer field and model field ' | |
| return SEGMENTORS.build( | |
| cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) | |