Instructions to use varcoder/CrackSeg-MIT-b0-dice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/CrackSeg-MIT-b0-dice with Transformers:
# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("varcoder/CrackSeg-MIT-b0-dice") model = SegformerForSemanticSegmentation.from_pretrained("varcoder/CrackSeg-MIT-b0-dice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bd5fde35cdad1e4c2b13908718ff6a44b0cb1312e9271a0752cf96bba9e76ea
- Size of remote file:
- 4.03 kB
- SHA256:
- 940f682d3161e1addb763a066d8d9b08568b82861919e31dbf45cbe53a590ee3
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