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:
- bcc42efb84df5ae8887c4dfb3527ed90adbf6e72c6b66de2f043f1a4a6a7da98
- Size of remote file:
- 4.09 kB
- SHA256:
- 5ea8175171c6e08a721025e31a79e17577edf56406d7bf95382ff0676580d8ea
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