---
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.

---

## 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