Update README.md

README.md

@@ -77,29 +77,32 @@ model-index:
 pipeline_tag: image-segmentation
 ---
 
-# FLAIR model collection
-
-The FLAIR models are a collection of semantic segmentation models initially developed to classify land cover on very high resolution aerial images (more specifically the French [BD ORTHO®](https://geoservices.ign.fr/bdortho) product). 
-The distributed pre-trained models differ in their :
-* dataset for training : [**FLAIR** dataset] (https://huggingface.co/datasets/IGNF/FLAIR) or the increased version of this dataset **FLAIR-INC** (x 3.5 patches). Only the FLAIR dataset is open at the moment.
-* input modalities : **RGB** (natural colours), **RGBI** (natural colours + infrared), **RGBIE** (natural colours + infrared + elevation)
-* model architecture : **resnet34_unet** (U-Net with a Resnet-34 encoder), **deeplab**
-* target class nomenclature : **12cl** (12 land cover classes) or **15cl** (15 land cover classes) 
-
-
-
-# FLAIR FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model
-
-The general characteristics of this specific model **FLAIR-INC_RVBIE_resnet34_unet_15cl_norm** are :
-* trained with the FLAIR-INC dataset 
-* RGBIE images  (true colours + infrared + elevation)
-* U-Net with a Resnet-34 encoder
-* 15 class nomenclature : [building, pervious_ surface, impervious_surface, bare_soil, water, coniferous, deciduous, brushwood, vineyard, herbaceous, agricultural_land, plowed_land, swimming pool, snow, greenhouse]
 
+<div style="border:0px; padding:25px; background-color:#F8F5F5; padding-top:10px; padding-bottom:1px;">
+  <h1>FLAIR model collection</h1>
+  <p>The FLAIR models are a collection of semantic segmentation models initially developed to classify land cover on very high resolution aerial images (more specifically the French <a href="https://geoservices.ign.fr/bdortho">BD ORTHO®</a> product). The distributed pre-trained models differ in their :</p>
+  <ul style="list-style-type:disc;">
+    <li>dataset for training : <a href="https://huggingface.co/datasets/IGNF/FLAIR"><b>FLAIR</b> dataset</a> or the increased version of this dataset <b>FLAIR-INC</b> (x 3.5 patches). Only the FLAIR dataset is open at the moment.</li>
+    <li>input modalities : <b>RGB</b> (natural colours), <b>RGBI</b> (natural colours + infrared), <b>RGBIE</b> (natural colours + infrared + elevation)</li>
+    <li>model architecture : <b>resnet34_unet</b> (U-Net with a Resnet-34 encoder), <b>deeplab</b></li>
+    <li>target class nomenclature : <b>12cl</b> (12 land cover classes) or <b>15cl</b> (15 land cover classes)</li>
+  </ul>
+</div>
+<br>
+
+<div style="border:0px; padding:25px; background-color:#FDFFF4 ; padding-top:10px; padding-bottom:1px;">
+  <h1>FLAIR FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model</h1> 
+  <p>The general characteristics of this specific model <strong>FLAIR-INC_RVBIE_resnet34_unet_15cl_norm</strong> are :</p>
+  <ul style="list-style-type:disc;">
+    <li>Trained with the FLAIR-INC dataset</li>
+    <li>RGBIE images (true colours + infrared + elevation)</li>
+    <li>U-Net with a Resnet-34 encoder</li>
+    <li>15 class nomenclature : [building, pervious surface, impervious surface, bare soil, water, coniferous, deciduous, brushwood, vineyard, herbaceous, agricultural land, plowed land, swimming pool, snow, greenhouse]</li>
+  </ul>
+</div>
 
 ## Model Informations
-
-- **Code repository:** https://github.com/IGNF/FLAIR-1-AI-Challenge
+- **Code repository:** https://github.com/IGNF/FLAIR-1
 - **Paper:** https://arxiv.org/pdf/2211.12979.pdf
 - **Developed by:** IGN
 - **Compute infrastructure:** 
@@ -107,7 +110,8 @@ The general characteristics of this specific model **FLAIR-INC_RVBIE_resnet34_un
     - hardware: HPC/AI resources provided by GENCI-IDRIS
 - **License:** : Apache 2.0
 
-  
+---
+
 ## Uses
 
 Although the model can be applied to other type of very high spatial earth observation images, it was initially developed to tackle the problem of classifying aerial images acquired on the French Territory.
@@ -148,15 +152,15 @@ _**Using the model on other spatial areas**_ :
 The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained on patches reprensenting the French Metropolitan territory. 
 The user should be aware that applying the model to other type of landscapes may imply a drop in model metrics.  
 
-
+---
 
 ## How to Get Started with the Model
 
-<!-- ANATOL }}-->
+Visit ([https://github.com/IGNF/FLAIR-1](https://github.com/IGNF/FLAIR-1)) to use the model.
+Fine-tuning and prediction tasks are detailed in the README file.
 
-Use the code below to get started with the model.
-{{ get_started_code | default("[More Information Needed]", true)}}
 
+---
 
 ## Training Details
 
@@ -166,14 +170,16 @@ Use the code below to get started with the model.
 The train/validation split was performed patchwise to obtain a 80% / 20% distribution between train and validation. 
 Annotation was performed at the _zone_ level (~100 patches per _zone_). Spatial independancy between patches is guaranted as patches from the same _zone_ were assigned to the same set (TRAIN or VALIDATION). 
 The following number of patches were used for train and validation :
-| TRAIN set        | 174 700 patches    |
+| TRAIN set            | 174 700 patches    |
 | VALIDATION set       | 43 700 patchs      |
 
 
-### Training Procedure
 
 
-#### Preprocessing [optional]
+
+### Training Procedure
+
+#### Preprocessing
 
 For traning the model, input normalization was performed to center-reduce (**a mean=0** and a **standard deviation = 1**, channel wise) the dataset.
 We used the statistics of TRAIN+VALIDATION for input normalization. It is recommended that the user apply the same type of input normalization. 
@@ -189,14 +195,9 @@ Statistics of the TRAIN+VALIDATION set :
 | Elevation Channel (E)   | 53.26       |79.30       |
 
 
-
-
-
 #### Training Hyperparameters
 
-- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-* Model architecture: Unet (implementation from the [Segmentation Models Pytorch library](https://segmentation-modelspytorch.readthedocs.io/en/latest/docs/api.html#unet)
+* Model architecture: Unet (implementation from the [Segmentation Models Pytorch library](https://segmentation-modelspytorch.readthedocs.io/en/latest/docs/api.html#unet))
 * Encoder : Resnet-34 pre-trained with ImageNet
 * Augmentation :
   * VerticalFlip(p=0.5)
@@ -215,8 +216,7 @@ Statistics of the TRAIN+VALIDATION set :
 * Class Weights : [1-building: 1.0 , 2-pervious surface: 1.0 , 3-impervious surface: 1.0 , 4-bare soil: 1.0 , 5-water: 1.0 , 6-coniferous: 1.0 , 7-deciduous: 1.0 , 8-brushwood: 1.0 , 9-vineyard: 1.0 , 10-herbaceous vegetation: 1.0 , 11-agricultural land: 1.0 , 12-plowed land: 1.0 , 13-swimming_pool: 1.0 , 14-snow: 1.0 , 15-clear cut: 0.0 , 16-mixed: 0.0 , 17-ligneous: 0.0 , 18-greenhouse: 1.0 , 19-other: 0.0]
 
 
-#### Speeds, Sizes, Times [optional]
-
+#### Speeds, Sizes, Times
 
 The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained on a HPC/AI resources provided by GENCI-IDRIS (Grant 2022-A0131013803). 
 16 V100 GPUs were used ( 4 nodes, 4 GPUS per node). With this configuration the approximate learning time is 6 minutes per epoch.
@@ -295,32 +295,35 @@ The following illustration gives the resulting confusion matrix :
 </div>
 
 
-
-
-
 ### Results
 
 Samples of results
 
 
-## Citation [optional]
+---
+
+## Citation
 
 
 **BibTeX:**
 
-@inproceeding{garioud2023flair,
+```
+@inproceedings{ign-flair,
       title={FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery}, 
       author={Anatol Garioud and Nicolas Gonthier and Loic Landrieu and Apolline De Wit and Marion Valette and Marc Poupée and Sébastien Giordano and Boris Wattrelos},
       year={2023},
-      eprint={2310.13336},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
+      booktitle={Advances in Neural Information Processing Systems (NeurIPS) 2023},
+      doi={https://doi.org/10.48550/arXiv.2310.13336},
 }
+```
 
 
 **APA:**
-Garioud, A., Gonthier, N., Landrieu, L., De Wit, A., Valette, M., Poupée, M., ... & Wattrelos, B. (2023). FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery. arXiv preprint arXiv:2310.13336.
-{{ citation_apa | default("[More Information Needed]", true)}}
-
-## Contact
-ai-challenge@ign.fr
+```
+Anatol Garioud, Nicolas Gonthier, Loic Landrieu, Apolline De Wit, Marion Valette, Marc Poupée, Sébastien Giordano and Boris Wattrelos. 2023. 
+FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery. (2023).
+In proceedings of Advances in Neural Information Processing Systems (NeurIPS) 2023.
+DOI: https://doi.org/10.48550/arXiv.2310.13336
+```
+
+## Contact : ai-challenge@ign.fr