Instructions to use Kavindu99/side-walk-segmentation-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kavindu99/side-walk-segmentation-model with Transformers:
# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Kavindu99/side-walk-segmentation-model") model = SegformerForSemanticSegmentation.from_pretrained("Kavindu99/side-walk-segmentation-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9af8acfa9a7571e362150eacd3d62e88d19815482391f9d53d54cef4ddaf9ed9
- Size of remote file:
- 15.1 MB
- SHA256:
- 1acf03955b7be1923df155afb4b2dc98b57b5971cb5b634543bd8f52eb3d1b34
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.