Instructions to use edwardhuang/test-carbonate-segmentation2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edwardhuang/test-carbonate-segmentation2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="edwardhuang/test-carbonate-segmentation2")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("edwardhuang/test-carbonate-segmentation2") model = SegformerForSemanticSegmentation.from_pretrained("edwardhuang/test-carbonate-segmentation2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc51799cfa91620d3b28b5224874966c882a76ea2f57759a60d21ed48c484d73
- Size of remote file:
- 3.58 kB
- SHA256:
- 16f327f3e1ea3285b7cfb70a262f5ba9f03a3c5fb89acdeeac53ef7c31caac25
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.