Instructions to use aholk/LN_segmentation_sweep_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aholk/LN_segmentation_sweep_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="aholk/LN_segmentation_sweep_v2")# Load model directly from transformers import UNetForSegmentation model = UNetForSegmentation.from_pretrained("aholk/LN_segmentation_sweep_v2", dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 9b5e92429d1b233ee7082982bc51625797a6de2a58958e092b053774e4daec6b
- Size of remote file:
- 211 kB
- SHA256:
- a9c6c8d44f904838712c08d1a6e6dc0fff72991834fbf281d5c4ec78eeb7dad9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.