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:
- 02045f2761f1d7dc4e05f1926a1ce6328bc2cc7425238ff9e2ad021bb49019ae
- Size of remote file:
- 110 MB
- SHA256:
- 606829c0add4334ad3e807a58e894fafe31983aa0bb20f65695add6a180e5ad1
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