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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc8fbe9c1e3cfad8e439da040b3e04165d4838999017b849056f7b5d80adaa2c
- Size of remote file:
- 110 MB
- SHA256:
- 7f03ce97ab5e705fb53ad868f84dd7721e62abb0167a9cfd455ada936ae7d52d
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