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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 676f635d3674dbe6d618ac86da18f954dc04febaa10eb0270695acdca48f455b
- Size of remote file:
- 133 MB
- SHA256:
- 4e4188c0ccfcbcd0f0b1910e78b6ece76c80ddc8900ee45a3cee5b2532ac53eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.