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README.md

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 license: mit
 pipeline_tag: image-segmentation
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+---
+# FraXet Baseline Models — U-Net and SegFormer for Automated Fracture Detection
+
+## Model Details
+**Developed by:** Ayoub Fatihi et al., UNIL (University of Lausanne)  
+**Model types:**  
+- **U-Net:** convolutional encoder–decoder with skip connections  
+- **SegFormer:** transformer-based encoder with lightweight MLP decoder 
+**License:** MIT  
+**Repository:** [github.com/ayoubft/fractex2d.pt](https://github.com/ayoubft/fractex2d.pt)  
+**Demo:** [huggingface.co/spaces/ayoubft/fractex2d](https://huggingface.co/spaces/ayoubft/fractex2d)
+**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.  
+**Out-of-scope:** Safety-critical or industrial deployment without expert validation.
+
+---
+
+## 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.