From 14d49cbfbf6413fa338940fbb051131d18cde0c2 Mon Sep 17 00:00:00 2001
From: Fabrice Daian <fabrice.daian@lis-lab.fr>
Date: Mon, 10 Mar 2025 10:08:47 +0100
Subject: [PATCH] restruct

---
 README.md | 24 ++++++++++++++----------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 6c3f2f6..e75a169 100644
--- a/README.md
+++ b/README.md
@@ -12,17 +12,22 @@ Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.s
 | ```metrology```               | [download](https://sync.lis-lab.fr/index.php/s/mYDRTeAQxMxNPPJ/download) | [download](https://sync.lis-lab.fr/index.php/s/degZsCxN7ZXxeB6/download) |
 
 
-#### ```Demo Notebooks on Google Colab```
 
-* **Use Case n°1:** Use a pre-trained µPIX model to denoise an image dataset (```metrology``` dataset) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EbV04iv141q7ebVUOErJupYboaCV40OD?usp=drive_link)
-* **Use Case n°2:** Train a µPIX model from scratch using a custom dataset (```metrology``` dataset) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1GaD6rjWGJsBr2dFy-WPLC3YjCZO4LJGl?usp=drive_link)
+|```Demo Notebooks on Google Colab```   |   |   |
+|---|---|---|
+| **Use case #1** | Use a pre-trained µPIX model to denoise an image dataset (```metrology``` dataset) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EbV04iv141q7ebVUOErJupYboaCV40OD?usp=drive_link)
+| **Use case #2** | Train a µPIX model from scratch using a custom dataset (```metrology``` dataset) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1GaD6rjWGJsBr2dFy-WPLC3YjCZO4LJGl?usp=drive_link)
 
 
-#### ```Reproductibility ``` 
-* **Code supporting Tables and Figures:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1x1NtdC0b-vgsjWv0oUbeRzDLmDwv6NiV?usp=drive_link)
 
 
-## Install µPIX Environment and Sources ([Tutorial VIDEO](https://sync.lis-lab.fr/index.php/s/GeNyYdLHMCRTfS9))
+|```Reproductibility ```   |  |
+|---|---|
+|**Ready-to-use Notebook supporting Tables and Figures presented in the paper** |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1x1NtdC0b-vgsjWv0oUbeRzDLmDwv6NiV?usp=drive_link)|
+
+
+
+## Install µPIX Environment and Sources ([Tutorial 🎬](https://sync.lis-lab.fr/index.php/s/GeNyYdLHMCRTfS9))
 <details>
   <summary>Click to expand</summary>
 
@@ -35,8 +40,6 @@ Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.s
 
 
 
-
-
 If you do not have already miniconda installed on your system, please refer to the [officical miniconda documentation](https://docs.anaconda.com/miniconda/install/). Then :
 
 1- Create a miniconda environment and source it:
@@ -68,7 +71,7 @@ pip install -r mupix/requirements.txt
 
 
 
-## Use Case #1 - Use a pre-trained µPIX model to denoise an image dataset ([Tutorial VIDEO](https://sync.lis-lab.fr/index.php/s/ftw9fdGacJyyfBq))
+## Use Case #1 - Use a pre-trained µPIX model to denoise an image dataset ([Tutorial 🎬](https://sync.lis-lab.fr/index.php/s/ftw9fdGacJyyfBq))
 
 <details>
   <summary>Click to expand</summary>
@@ -181,7 +184,8 @@ Once completed, the denoised images are stored in the ```./experiments/metrology
 </details>
 
 
-## Use Case #2 - Train a µPIX model from scratch using a custom dataset
+## Use Case #2 - Train a µPIX model from scratch using a custom dataset ([Tutorial 🎬](https://sync.lis-lab.fr/index.php/s/pb5jPqNtTgwFSjB))
+
 
 <details>
   <summary>Click to expand</summary>
-- 
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