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