Instructions to use jafdxc/regular-segmentation-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jafdxc/regular-segmentation-model with Transformers:
# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("jafdxc/regular-segmentation-model") model = SegformerForSemanticSegmentation.from_pretrained("jafdxc/regular-segmentation-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d120dc44acf0538c15b58bf7d8f3fce535669f5ed59f1677b4c0206f4ba7c09b
- Size of remote file:
- 14.9 MB
- SHA256:
- e868723953335a47afac534fe4025b4b4ada43d77e87f265aed2353fa3694548
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.