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:
- 583d9216b27187f96ff6bd5dda5563d77329453429b6069c138744875968b6bf
- Size of remote file:
- 533 MB
- SHA256:
- 58d502811b330dfcd3a1de356966d3a78e1ee7c2bc4f4c93e398f5239ee16147
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.