Clean out template readme

README.md

@@ -39,4 +39,92 @@ model-index:
     - name: IoU Permanent Structure
       type: IoU
       value: 59.9
----
+---
+
+<div style="border:1px solid black; padding:25px; background-color:#FDFFF4 ; padding-top:10px; padding-bottom:1px;">
+  <h1>FRACTAL-LidarHD_7cl_randlanet</h1> 
+  <p>The general characteristics of this specific model <strong>FRACTAL-LidarHD_7cl_randlanet</strong> are :</p>
+  <ul style="list-style-type:disc;">
+    <li>Trained with the FRACTAL dataset</li>
+    <li>Aerial lidar point clouds, colorized with rgb + near-infrared, with high point density (~40 pts/m²)</li>
+    <li>RandLa-Net architecture as implemented in the Myria3D library</li>
+    <li>7 class nomenclature : [other, ground, vegetation, building, water, bridge, permanent structure]</li>
+  </ul>
+</div>
+
+## Model Informations
+- **Code repository:** https://github.com/IGNF/myria3d (V3.8)
+- **Paper:** TBD
+- **Developed by:** IGN
+- **Compute infrastructure:** 
+    - software: python, pytorch-lightning
+    - hardware: in-house HPC/AI resources
+- **License:** : Etalab 2.0
+
+---
+
+## Uses
+
+
+
+## Bias, Risks, Limitations and Recommendations
+
+---
+
+## How to Get Started with the Model
+
+Visit ([https://github.com/IGNF/FLAIR-1](https://github.com/IGNF/myria3d)) to use the model.
+Fine-tuning and prediction tasks are detailed in the README file.
+
+
+---
+
+## Training Details
+
+### Training Data
+
+
+### Training Procedure
+
+#### Preprocessing
+
+
+
+#### Training Hyperparameters
+
+
+#### Speeds, Sizes, Times
+
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+#### Metrics
+
+
+### Results
+
+Samples of results
+
+
+---
+
+## Citation
+
+
+**BibTeX:**
+
+```
+
+```
+
+
+**APA:**
+```
+
+```
+
+## Contact : TBD