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---
license: mit
pipeline_tag: image-segmentation
---
# Models - U-Net and SegFormer for Automated Fracture Detection trained on FraXet
## Model Details
**Model types:**
- **U-Net:** convolutional encoder–decoder with skip connections [<cite>[Ronneberger et al. 2015][1]</cite>]
- **SegFormer:** transformer-based encoder with lightweight MLP decoder [<cite>[Xie et al. 2021][2]</cite>
] (implemented by [smp](\url{https://github.com/qubvel/segmentation_models.pytorch}))
**License:** MIT
**Dataset:** FraXet (zenodo)
**Repository:** [github.com/ayoubft/fractex2d.pt](https://github.com/ayoubft/fractex2d.pt)
**Demo:** [huggingface.co/spaces/ayoubft/fractex2d](https://huggingface.co/spaces/ayoubft/fractex2D_tuto)
**Paper:** coming soon
---
## Model Description
These models perform **pixel-wise fracture segmentation** from paired RGB and DEM patches of outcrop imagery.
They serve as **baseline architectures** in the *FraXet* benchmarking framework comparing classical filters, CNNs, and transformer models for geological fracture mapping.
---
## Uses
**Direct use:** Predict fracture probability maps or binary masks for UAV or field imagery (RGB + DEM).
**Downstream use:** Use as baseline models or assistive pre-annotation tools for geoscience datasets.
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## Bias, Risks, and Limitations
Predictions depend on annotation quality, illumination, and lithology.
Thin or poorly illuminated fractures may be missed; shadows and texture can yield false positives.
Use predictions as assistive probability maps and validate with expert interpretation.
[1]: https://doi.org/10.1007/978-3-319-24574-4_28
[2]: https://doi.org/10.48550/arXiv.2105.15203 |