Instructions to use Subh775/Dis-Seg-Former with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subh775/Dis-Seg-Former with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Subh775/Dis-Seg-Former")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Subh775/Dis-Seg-Former", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94eb185690e338e7daf306ca50b9c58a7b620ab12ff6be409b8de4ff6921d336
- Size of remote file:
- 533 MB
- SHA256:
- 34f3833508adcb44e1ec68fe25d4a92fa601545a1caf0f66b3d01334d84ed559
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.