@@ -1,4 +1,4 @@
-<div align="center">
+<div align="center" markdown="1">
   <img src="docs/assets/SG_img/SG - Horizontal Glow 2.png" width="600"/>
  <br/><br/>
   
@@ -7,8 +7,9 @@
 
 #### Version 3 is out! Notebooks have been updated!
 ______________________________________________________________________
-  
-  <p align="center">
+</div>  
+<div align="center">
+<p align="center">
   <a href="https://www.supergradients.com/">Website</a> •
   <a href="https://deci-ai.github.io/super-gradients/welcome.html">Docs</a> •
   <a href="#getting-started">Getting Started</a> •
@@ -25,10 +26,12 @@ ______________________________________________________________________
   <a href="https://github.com/Deci-AI/super-gradients/releases"><img src="https://img.shields.io/github/v/release/Deci-AI/super-gradients" />
   <a href="https://join.slack.com/t/supergradients-comm52/shared_invite/zt-10vz6o1ia-b_0W5jEPEnuHXm087K~t8Q"><img src="https://img.shields.io/badge/slack-community-blueviolet" />
   <a href="https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.md"><img src="https://img.shields.io/badge/license-Apache%202.0-blue" />
-  <a href="https://deci-ai.github.io/super-gradients/welcome.html"><img src="https://img.shields.io/badge/docs-sphinx-brightgreen" />
+  <a href="https://deci-ai.github.io/super-gradients/welcome.html"><img src="https://img.shields.io/badge/docs-sphinx-brightgreen" /></a>
 </p>    
 </div>
 
+______________________________________________________________________
+
 ## Build with SuperGradients
 __________________________________________________________________________________________________________
 
@@ -310,7 +313,7 @@ Recipes support out of the box every model, metric or loss that is implemented i
  </br></br>
 
  </br>
-<details>
+<details markdown="1">
   <summary><h3>Using Distributed Data Parallel (DDP) </h3></summary>
 
 #### Why use DDP ?
@@ -422,7 +425,7 @@ There is no clear rule, but a rule of thumb seems to be to [linearly increase th
 
 </details>
 
-<details>
+<details markdown="1">
 <summary><h3> Easily change architectures parameters </h3></summary>
 
 ```python
@@ -440,7 +443,7 @@ backbone_resnet18 = models.get(model_name="resnet18", arch_params={"backbone_mod
 
 </details>
 
-<details>
+<details markdown="1">
 
 <summary><h3> Using phase callbacks </h3></summary>  
   
@@ -469,7 +472,7 @@ train_params = {"phase_callbacks": phase_callbacks}
 
 </details>
 
-<details>
+<details markdown="1">
 
 <summary><h3> Integration to DagsHub </h3></summary>    
 
@@ -521,7 +524,7 @@ train_params = { ... # training parameters
 
 </details>
 
-<details>
+<details markdown="1">
 
 <summary><h3> Integration to ClearML </h3></summary>    
     
@@ -549,7 +552,7 @@ train_params = { ... # training parameters
 ## Installation Methods
 __________________________________________________________________________________________________________
 ### Prerequisites
-<details>
+<details markdown="1">
   
 <summary>General requirements</summary>
   
@@ -560,7 +563,7 @@ ________________________________________________________________________________
 
 </details>
     
-<details>
+<details markdown="1">
   
 <summary>To train on nvidia GPUs</summary>
   
@@ -572,7 +575,7 @@ ________________________________________________________________________________
     
 ### Quick Installation
 
-<details>
+<details markdown="1">
   
 <summary>Install stable version using PyPi</summary>
 
@@ -585,7 +588,7 @@ That's it !
 
 </details>
     
-<details>
+<details markdown="1">
   
 <summary>Install using GitHub</summary>
 

site_name: super-gradients
nav:
- Welcome:
  - Intro: ./documentation/source/welcome.md
  - Installation: ./documentation/source/installation.md
  - Model Zoo: ./documentation/source/model_zoo.md
- Quick Start:
  - Basic: ./documentation/source/QuickstartBasicToolkit.md
  - Classification: ./documentation/source/Example_Classification.md
  - Object Detection: ./documentation/source/ObjectDetection.md
  - Segmentation: ./documentation/source/Segmentation.md
  - Pose Estimation: ./documentation/source/PoseEstimation.md
  - Training an external model: ./documentation/source/Example_Training-an-external-model.md
- Tutorials:
  - Models: ./documentation/source/models.md
  - Dataset:
    - Data: ./documentation/source/Data.md
    - Computer Vision Datasets: ./src/super_gradients/training/datasets/Dataset_Setup_Instructions.md
  - Loss functions: ./documentation/source/Losses.md
  - LR schedulers: ./documentation/source/LRScheduling.md
  - Metrics: ./documentation/source/Metrics.md
  - Optimizers: ./documentation/source/optimizers.md
  - Phase Callbacks: ./documentation/source/PhaseCallbacks.md
  - YAMLs and Recipes:
    - Configurations: ./documentation/source/configuration_files.md
    - Recipes: ./src/super_gradients/recipes/Training_Recipes.md
  - Checkpoints: ./documentation/source/Checkpoints.md
  - Docker: ./documentation/source/SGDocker.md
  - Output Adapter: ./documentation/source/DetectionOutputAdapter.md
- Features:
  - Training Modes: ./documentation/source/device.md
  - Logging: ./documentation/source/logs.md
  - Experiment Monitoring: ./documentation/source/experiment_monitoring.md
  - Exponential Moving Average (EMA): ./documentation/source/EMA.md
  - Automatic Mixed Precision (AMP): ./documentation/source/average_mixed_precision.md
  - Knowledge Distillation (KD): ./documentation/source/KD.md
  - Quantization (PTQ & QAT): ./documentation/source/ptq_qat.md
- Troubleshooting: ./documentation/source/troubleshooting.md
- Contribution: ./documentation/source/CONTRIBUTING.md
- Code: this_is_automatically_generated.md