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--- a/README.md
+++ b/README.md
@@ -1,16 +1,10 @@
-# Code & Data Repository
+# Code, Models & Data Repository
 
 ## *μPIX : Leveraging Generative AI for Enhanced, Personalized and Sustainable Microscopy*
-### Gabriel Bon, Daniel Sapede, Cédric Matthews, and Fabrice Daian
-
-----
-----
-
-|                          | Paper  |
-|--------------------------|--------------------------------------------------------------------------------------------------------------------------------|
-| Preprint                | [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.svg)](https://doi.org/10.1101/2024.10.25.620201) |
-| Paper                   | To be published |
+Preprint BioRXiv [![DOI:10.1101/2024.10.25.620201](images/badge_preprint_mupix.svg)](https://doi.org/10.1101/2024.10.25.620201)
+#### Gabriel Bon, Daniel Sapede, Cédric Matthews, and Fabrice Daian
 
+##### <u>Dataset & pre-trained µPIX Models</u>
 |                          | Dataset | pre-trained µPIX Models |
 |--------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|
 | CSBDeep                 | [download](https://sync.lis-lab.fr/index.php/s/sy3SrGgqNafbP5X/download) | [download](https://sync.lis-lab.fr/index.php/s/9SxkR2QH3Z79Bbc/download) |
@@ -18,11 +12,17 @@
 | 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  |  |
-|--------------------------|--------------------------------------------------------------------------------------------------------------------------------|
-| Inference on Metrology dataset using pre-trained µPIX model               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EbV04iv141q7ebVUOErJupYboaCV40OD?usp=drive_link) |
+##### <u>Demo Notebooks</u>
+
+* Use a pre-trained µPIX model to denoise images (metrology dataset) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EbV04iv141q7ebVUOErJupYboaCV40OD?usp=drive_link)
+* Train a µPIX model from scratch (metrology dataset)
 
-|                          | Code & System Requirements  |
+
+## Install required µPIX Environment and Sources ([Tutorial VIDEO](https://sync.lis-lab.fr/index.php/s/GeNyYdLHMCRTfS9))
+<details>
+  <summary>Click to expand</summary>
+
+|  Requirements |   |
 |--------------------------|--------------------------------------------------------------------------------------------------------------------------------|
 | Python                | 3.11 |
 | Tensorflow                   | 2.14 |
@@ -30,10 +30,6 @@
 | OS                   | Linux |
 
 
-----
-----
-
-## Install required µPIX Environment and Sources ([Tutorial VIDEO](https://sync.lis-lab.fr/index.php/s/GeNyYdLHMCRTfS9))
 
 
 
@@ -61,7 +57,7 @@ and then proceed to the installation of required Python packages:
 ```bash
 pip install -r mupix/requirements.txt
 ```
-
+</details>
 
 
 
@@ -69,6 +65,9 @@ pip install -r mupix/requirements.txt
 
 ## Use Case n°1 - Use a pre-trained µPIX model to denoise an image dataset ([Tutorial VIDEO](https://sync.lis-lab.fr/index.php/s/ftw9fdGacJyyfBq))
 
+<details>
+  <summary>Click to expand</summary>
+
 µPIX comes with 3 ptre-trained models along with their respective datasets:
 
 |                          | Dataset | pre-trained µPIX Models |
@@ -174,9 +173,14 @@ python mupixinfer.py --experiment_path "./experiments/metrology_experiment/"
 
 Once finished, the denoised images are stored inside ```./experiments/metrology_experiment/predictions/``` directory.
 
+</details>
+
 
 ## Use case n°2 - Train a µPIX model from scratch using a custom dataset
 
+<details>
+  <summary>Click to expand</summary>
+
 Move to the `mupix` directory:
 ```bash
 cd mupix
@@ -348,8 +352,6 @@ If the training stops because it reached the maximum number of epochs defined in
 python mupixtraining.py --experiment_path "./experiments/metrology_experiment" --retrain
 ```
 
-
-
 ### 2.4 - Inference on the test dataset
 
 The `mupixinfer.py` script allows you to use a pre-trained µPIX model to denoise a dataset located in the `test` directory inside the experiment path.
@@ -374,7 +376,7 @@ Saved prediction: ./experiments/metrology_experiment/predictions/X_StageData0001
 All predictions saved successfully!
 Data predicted at ./experiments/metrology_experiment/predictions !
 ```
-
+</details>