@@ -1,21 +1,19 @@
 <div align="center">
-  <img src="docs/assets/SG_img/SG - Horizontal.png" width="600"/>
+  <img src="assets/SG_img/SG - Horizontal Glow 2.png" width="600"/>
  <br/><br/>
   
-**Easily train or fine-tune SOTA computer vision models with one open source training library**
+**Build, train, and fine-tune production-ready deep learning  SOTA vision models**
 [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Easily%20train%20or%20fine-tune%20SOTA%20computer%20vision%20models%20from%20one%20training%20repository&url=https://github.com/Deci-AI/super-gradients&via=deci_ai&hashtags=AI,deeplearning,computervision,training,opensource)
 
-#### Fill our 4-question quick survey! We will raffle free SuperGradients swag between those who will participate -> [Fill Survey](https://hz8qtlvwkaw.typeform.com/to/OpKda0Qe)
+#### Version 3 is out! Notebooks have been updated!
 ______________________________________________________________________
   
   <p align="center">
   <a href="https://www.supergradients.com/">Website</a> •
-  <a href="#why-use-supergradients">Why Use SG?</a> •
   <a href="https://deci-ai.github.io/super-gradients/user_guide.html#introducing-the-supergradients-library">User Guide</a> •
   <a href="https://deci-ai.github.io/super-gradients/super_gradients.common.html">Docs</a> •
-  <a href="#getting-started">Getting Started Notebooks</a> •
-  <a href="#transfer-learning">Transfer Learning</a> •  
-  <a href="#computer-vision-models---pretrained-checkpoints">Pretrained Models</a> •
+  <a href="#getting-started">Getting Started</a> •
+  <a href="#implemented-model-architectures">Pretrained Models</a> •
   <a href="#community">Community</a> •
   <a href="#license">License</a> •
   <a href="#deci-platform">Deci Platform</a>
@@ -24,7 +22,7 @@ ______________________________________________________________________
   <a href="https://github.com/Deci-AI/super-gradients#prerequisites"><img src="https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue" />
   <a href="https://github.com/Deci-AI/super-gradients#prerequisites"><img src="https://img.shields.io/badge/pytorch-1.9%20%7C%201.10-blue" />
   <a href="https://pypi.org/project/super-gradients/"><img src="https://img.shields.io/pypi/v/super-gradients" />
-  <a href="https://github.com/Deci-AI/super-gradients#computer-vision-models-pretrained-checkpoints" ><img src="https://img.shields.io/badge/pre--trained%20models-30-brightgreen" />
+  <a href="https://github.com/Deci-AI/super-gradients#computer-vision-models-pretrained-checkpoints" ><img src="https://img.shields.io/badge/pre--trained%20models-34-brightgreen" />
   <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" />
@@ -32,80 +30,113 @@ ______________________________________________________________________
 </p>    
 </div>
 
+[](https://deci-ai.github.io/super-gradients/user_guide.html#introducing-the-supergradients-library)
 
-# SuperGradients
+## Build with SuperGradients
+__________________________________________________________________________________________________________
 
-## Introduction
-Welcome to SuperGradients, a free, open-source training library for PyTorch-based deep learning models.
-SuperGradients allows you to train or fine-tune SOTA pre-trained models for all the most commonly applied computer vision tasks with just one training library. We currently support object detection, image classification and semantic segmentation for videos and images.
+### Support various computer vision tasks
+<div align="center">
+<img src="./assets/SG_img/Segmentation 1500x900 .png" width="250px">
+<img src="./assets/SG_img/Object detection 1500X900.png" width="250px">
+<img src="./assets/SG_img/Classification 1500x900.png" width="250px">
+</div>
 
-Docs and full user guide[](#)
-### Why use SuperGradients?
- 
-**Built-in SOTA Models**
 
-Easily load and fine-tune production-ready, [pre-trained SOTA models](https://github.com/Deci-AI/super-gradients#pretrained-classification-pytorch-checkpoints) that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy.
-    
-**Easily Reproduce our Results**
-       
-Why do all the grind work, if we already did it for you? leverage tested and proven [recipes](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes) & [code examples](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples) for a wide range of computer vision models generated by our team of deep learning experts. Easily configure your own or use plug & play hyperparameters for training, dataset, and architecture.
-    
-**Production Readiness and Ease of Integration**
-    
-All SuperGradients models’ are production ready in the sense that they are compatible with deployment tools such as TensorRT (Nvidia) and OpenVINO (Intel) and can be easily taken into production. With a few lines of code you can easily integrate the models into your codebase.
+### Ready to deploy pre-trained SOTA models
+```python
+# Load model with pretrained weights
+model = models.get("yolox_s", pretrained_weights="coco")
+```
 
+#### Classification
 <div align="center">
-<img src="./docs/assets/SG_img/detection-demo.png" width="600px">
+<img src="./assets/SG_img/Classification@2xDark.png" width="800px">
 </div>
 
+#### Semantic Segmentation
+<div align="center">
+<img src="./assets/SG_img/Semantic Segmentation@2xDark.png" width="800px">
+</div>
+
+#### Object Detection 
+<div align="center">
+<img src="./assets/SG_img/Object Detection@2xDark.png" width="800px">
+</div>
+
+
+
+All Computer Vision Models - Pretrained Checkpoints can be found [here](src/super_gradients/training/Computer_Vision_Models_Pretrained_Checkpoints.md)
+
+
+### Easy to train SOTA Models
+
+Easily load and fine-tune production-ready, pre-trained SOTA models that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy. 
+For more information on how to do it go to [Getting Started](#getting-started)
     
+
+### Plug and play recipes
+```python
+python -m super_gradients.train_from_recipe --config-name=imagenet_regnetY architecture=regnetY800 dataset_interface.data_dir=<YOUR_Imagenet_LOCAL_PATH> ckpt_root_dir=<CHEKPOINT_DIRECTORY>
+```
+More example on how and why to use recipes can be found in [Recipes](#recipes)
+
+
+### Production readiness
+All SuperGradients models’ are production ready in the sense that they are compatible with deployment tools such as TensorRT (Nvidia) and OpenVINO (Intel) and can be easily taken into production. With a few lines of code you can easily integrate the models into your codebase.
+```python
+# Load model with pretrained weights
+model = models.get("yolox_s", pretrained_weights="coco")
+
+# Prepare model for conversion
+# Input size is in format of [Batch x Channels x Width x Height] where 640 is the standart COCO dataset dimensions
+model.eval()
+model.prep_model_for_conversion(input_size=[1, 3, 640, 640])
+    
+# Create dummy_input
+
+# Convert model to onnx
+torch.onnx.export(model, dummy_input,  "yolox_s.onnx")
+```
+More information on how to take your model to production can be found in [Getting Started](#getting-started) notebooks
+
+## Quick Installation
+
+__________________________________________________________________________________________________________
+
+ 
+```bash
+pip install super-gradients
+```
+
 ## What's New
-* 【07/08/2022】DDRNet23 -  new pre-trained [checkpoints](https://github.com/Deci-AI/super-gradients#pretrained-semantic-segmentation-pytorch-checkpoints) and [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes) for Cityscapes with SOTA mIoU scores (~1% above paper)🎯
+__________________________________________________________________________________________________________
+* 【06/9/2022】 PP-LiteSeg - new pre-trained [checkpoints](src/super_gradients/training/Computer_Vision_Models_Pretrained_Checkpoints.md)  for Cityscapes with SOTA mIoU scores (~1.5% above paper)🎯
+* 【07/08/2022】DDRNet23 -  new pre-trained [checkpoints](src/super_gradients/training/Computer_Vision_Models_Pretrained_Checkpoints.md) and [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes) for Cityscapes with SOTA mIoU scores (~1% above paper)🎯
 * 【27/07/2022】YOLOX models (object detection) - recipes and pre-trained checkpoints.
-* 【07/07/2022】SSD Lite MobileNet V2,V1 - Training [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes/coco_ssd_lite_mobilenet_v2.yaml) and pre-trained [checkpoints](https://github.com/Deci-AI/super-gradients#pretrained-object-detection-pytorch-checkpoints) on COCO - Tailored for edge devices! 📱
-* 【07/07/2022】 STDC  - new pre-trained [checkpoints](https://github.com/Deci-AI/super-gradients#pretrained-semantic-segmentation-pytorch-checkpoints) and [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes) for Cityscapes with super SOTA mIoU scores (~2.5% above paper)🎯
-* 【16/06/2022】 ResNet50  - new pre-trained checkpoint and [recipe](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes/imagenet_resnet50_kd.yaml) for ImageNet top-1 score of 81.9 💪
-* 【09/06/2022】 ViT models (Vision Transformer) - Training [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes) and pre-trained [checkpoints](https://github.com/Deci-AI/super-gradients#pretrained-object-detection-pytorch-checkpoints) (ViT, BEiT).
-* 【09/06/2022】 Knowledge Distillation support - [training module](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/kd_model/kd_model.py) and [notebook](https://bit.ly/3HQvbsg).
-* 【06/04/2022】 Integration with professional tools - [Weights and Biases](https://bit.ly/3BJzCUv) and [DagsHub](https://bit.ly/3bznLhc).
-* 【09/03/2022】 New [quick start](#quick-start-notebook---semantic-segmentation) and [transfer learning](#transfer-learning-with-sg-notebook---semantic-segmentation) example notebooks for Semantic Segmentation.
-* 【07/02/2022】 We added RegSeg recipes and pre-trained models to our [Semantic Segmentation models](#pretrained-semantic-segmentation-pytorch-checkpoints).
-* 【01/02/2022】 We added issue templates for feature requests and bug reporting.
-* 【20/01/2022】 STDC family - new recipes added with even higher mIoU💪
+* 【07/07/2022】SSD Lite MobileNet V2,V1 - Training [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes/coco_ssd_lite_mobilenet_v2.yaml) and pre-trained [checkpoints](src/super_gradients/training/Computer_Vision_Models_Pretrained_Checkpoints.md) on COCO - Tailored for edge devices! 📱
+* 【07/07/2022】 STDC  - new pre-trained [checkpoints](src/super_gradients/training/Computer_Vision_Models_Pretrained_Checkpoints.md) and [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes) for Cityscapes with super SOTA mIoU scores (~2.5% above paper)🎯
 
 Check out SG full [release notes](https://github.com/Deci-AI/super-gradients/releases).
 
 ## Coming soon
+__________________________________________________________________________________________________________
+- [ ] PP-LiteSeg recipes for Cityscapes with SOTA mIoU scores (~1.5% above paper)🎯
 - [ ] Single class detectors (recipes, pre-trained checkpoints) for edge devices deployment.
 - [ ] Single class segmentation (recipes, pre-trained checkpoints) for edge devices deployment.
 - [ ] QAT capabilities (Quantization Aware Training).
-- [ ] Dali implementation.
 - [ ] Integration with more professional tools.
-- [ ] Improved pre-trained checkpoints and recipes (DDRNet, ResNet, RegSeg, etc.)
 
-__________________________________________________________________________________________________________
-### Table of Content
 
+## Table of Content
+__________________________________________________________________________________________________________
 <!-- toc -->
 
 - [Getting Started](#getting-started)
-    - [Quick Start Notebook - Classification example](#quick-start-notebook---classification)
-    - [Quick Start Notebook - Semantic segmentation example](#quick-start-notebook---semantic-segmentation)
-<!-- - [Quick Start Notebook - Object detection example](#quick-start-notebook---object-detection)
-- [Walkthrough Notebook](#supergradients-complete-walkthrough-notebook)
-- [Transfer Learning with SG Notebook - Object detection example](#transfer-learning-with-sg-notebook---object-detection)
-    - [Quick Start Notebook - Upload to Deci Platform example](#quick-start-notebook---upload-your-model-to-deci-platform) -->
-- [Transfer Learning](#transfer-learning)  
-    - [Transfer Learning with SG Notebook - Semantic segmentation example](#transfer-learning-with-sg-notebook---semantic-segmentation)
-- [Knowledge Distillation Training](#knowledge-distillation-training)  
-    - [Knowledge Distillation Training Quick Start with SG Notebook - ResNet18 example](#knowledge-distillation-training-quick-start-with-sg-notebook---resnet18-example)
+- [Advanced Features](#advanced-features)
 - [Installation Methods](#installation-methods)
     - [Prerequisites](#prerequisites)
     - [Quick Installation](#quick-installation)
-- [Computer Vision Models - Pretrained Checkpoints](#computer-vision-models---pretrained-checkpoints)
-  - [Pretrained Classification PyTorch Checkpoints](#pretrained-classification-pytorch-checkpoints)
-  - [Pretrained Object Detection PyTorch Checkpoints](#pretrained-object-detection-pytorch-checkpoints)
-  - [Pretrained Semantic Segmentation PyTorch Checkpoints](#pretrained-semantic-segmentation-pytorch-checkpoints)
 - [Implemented Model Architectures](#implemented-model-architectures)
 - [Contributing](#contributing)
 - [Citation](#citation)
@@ -116,6 +147,7 @@ ________________________________________________________________________________
 <!-- tocstop -->
 
 ## Getting Started
+__________________________________________________________________________________________________________
 
 ### Start Training with Just 1 Command Line
 The most simple and straightforward way to start training SOTA performance models with SuperGradients reproducible recipes. Just define your dataset path and where you want your checkpoints to be saved and you are good to go from your terminal!
@@ -124,172 +156,216 @@ The most simple and straightforward way to start training SOTA performance model
 python -m super_gradients.train_from_recipe --config-name=imagenet_regnetY architecture=regnetY800 dataset_interface.data_dir=<YOUR_Imagenet_LOCAL_PATH> ckpt_root_dir=<CHEKPOINT_DIRECTORY>
 ```
 ### Quickly Load Pre-Trained Weights for Your Desired Model with SOTA Performance
-Want to try our pre-trained models on your machine? Import SuperGradients, initialize your SgModel, and load your desired architecture and pre-trained weights from our [SOTA model zoo](#computer-vision-models---pretrained-checkpoints)
-    
+Want to try our pre-trained models on your machine? Import SuperGradients, initialize your Trainer, and load your desired architecture and pre-trained weights from our [SOTA model zoo](src/super_gradients/training/Computer_Vision_Models_Pretrained_Checkpoints.md)
+
 ```python
 # The pretrained_weights argument will load a pre-trained architecture on the provided dataset
-# This is an example of loading COCO-2017 pre-trained weights for a YOLOX Nano object detection model
     
 import super_gradients
-from super_gradients.training import SgModel
 
-trainer = SgModel(experiment_name="yoloxn_coco_experiment",ckpt_root_dir=<CHECKPOINT_DIRECTORY>)
-trainer.build_model(architecture="yolox_n", arch_params={"pretrained_weights": "coco", num_classes": 80})
-```   
-    
-### Quick Start Notebook - Classification
+model = models.get("model-name", pretrained_weights="pretrained-model-name")
 
-Get started with our quick start notebook for image classification tasks on Google Colab for a quick and easy start using free GPU hardware.
-
-<table class="tfo-notebook-buttons" align="left">
- <td>
-   <a target="_blank" href="https://bit.ly/3ufnsgT"><img src="./docs/assets/SG_img/colab_logo.png" />Classification Quick Start in Google Colab</a>
- </td>
-  <td>
-   <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_classification.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
- </td>
- <td>
-   <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
+```   
+###  Classification
+
+#### Transfer Learning 
+  <table class="tfo-notebook-buttons" align="left">
+ <td width="500">  
+  <a target="_blank" href="https://bit.ly/3xzIutb"><img src="./assets/SG_img/colab_logo.png" /> Classification Transfer Learning</a>
+  </td>
+ <td width="200">    
+ <a target="_blank" href="https://bit.ly/3xwYEn1"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
 </table>
  </br></br>
 
 
-### Quick Start Notebook - Semantic Segmentation
-
-Get started with our quick start notebook for semantic segmentation tasks on Google Colab for a quick and easy start using free GPU hardware.
+###  Semantic Segmentation
 
+####  Quick Start 
 <table class="tfo-notebook-buttons" align="left">
- <td>
-   <a target="_blank" href="https://bit.ly/3Jp7w1U"><img src="./docs/assets/SG_img/colab_logo.png" />Segmentation Quick Start in Google Colab</a>
- </td>
-  <td>
-   <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_segmentation.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
+ <td width="500">
+<a target="_blank" href="https://bit.ly/3qKx9m8"><img src="./assets/SG_img/colab_logo.png" /> Segmentation Quick Start</a>
  </td>
- <td>
-   <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
+ <td width="200">
+<a target="_blank" href="https://bit.ly/3qJjxYq"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source </a>
  </td>
 </table>
  </br></br>
 
-<!-- 
-### Quick Start Notebook - Object Detection
-
-Get started with our quick start notebook for object detection tasks on Google Colab for a quick and easy start using free GPU hardware.
 
+ 
+ ####  Transfer Learning 
 <table class="tfo-notebook-buttons" align="left">
- <td>
-   <a target="_blank" href="https://bit.ly/3wqMsEM"><img src="./docs/assets/SG_img/colab_logo.png" />Detection Quick Start in Google Colab</a>
+ <td width="500">
+<a target="_blank" href="https://bit.ly/3qKwMbe"><img src="./assets/SG_img/colab_logo.png" /> Segmentation Transfer Learning</a>
  </td>
-  <td>
-   <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_detection.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
- </td>
- <td>
-   <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
+ <td width="200">
+<a target="_blank" href="https://bit.ly/3ShJlXn"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
 </table>
  </br></br>
- 
-### Quick Start Notebook - Upload your model to Deci Platform
 
-Get Started with an example of how to upload your trained model to Deci Platform for runtime optimization and compilation to your target deployment HW.
-<table class="tfo-notebook-buttons" align="left">
-  <tbody>
-    <tr>
-      <td vertical-align="middle">
-        <img src="./docs/assets/SG_img/colab_logo.png" />
-        <a target="_blank" href="https://bit.ly/3cAkoXG">
-          Upload to Deci Platform in Google Colab
-        </a>
-      </td>
-      <td vertical-align="middle">
-        <img src="./docs/assets/SG_img/download_logo.png" />
-        <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_model_upload_deci_lab.ipynb">
-          Download notebook
-        </a>
-      </td>
-      <td>
-        <img src="./docs/assets/SG_img/GitHub_logo.png" />
-        <a target="_blank" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/deci_lab_export_example/deci_lab_export_example.py">
-          View source on GitHub
-        </a>
-      </td>
-    </tr>
-  </tbody>
-</table>
- </br></br>
-
-### SuperGradients Complete Walkthrough Notebook
 
-Learn more about SuperGradients training components with our walkthrough notebook on Google Colab for an easy to use tutorial using free GPU hardware
 
-<table class="tfo-notebook-buttons" align="left">
- <td>
-   <a target="_blank" href="https://bit.ly/3JspSPF"><img src="./docs/assets/SG_img/colab_logo.png" />SuperGradients Walkthrough in Google Colab</a>
- </td>
-  <td>
-   <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_Walkthrough.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
- </td>
- <td>
-   <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
+####  How to Connect Custom Dataset 
+  <table class="tfo-notebook-buttons" align="left">
+ <td width="500"> 
+<a target="_blank" href="https://bit.ly/3QQBVJp"><img src="./assets/SG_img/colab_logo.png" /> Segmentation How to Connect Custom Dataset</a>
+   </td>
+ <td width="200">
+ <a target="_blank" href="https://bit.ly/3Us2WGi"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
 </table>
  </br></br>
 
- ### Transfer Learning with SG Notebook - Object Detection
 
-Learn more about SuperGradients transfer learning or fine tuning abilities with our COCO pre-trained YoloX nano fine tuning into a sub-dataset of PASCAL VOC example notebook on Google Colab for an easy to use tutorial using free GPU hardware
 
-<table class="tfo-notebook-buttons" align="left">
- <td>
-   <a target="_blank" href="https://bit.ly/3iGvnP7"><img src="./docs/assets/SG_img/colab_logo.png" />Detection Transfer Learning in Google Colab</a>
- </td>
-  <td>
-   <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_transfer_learning_object_detection.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
- </td>
- <td>
-   <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
+###  Object Detection
+
+
+#### Transfer Learning
+  <table class="tfo-notebook-buttons" align="left">
+ <td width="500">   
+<a target="_blank" href="https://bit.ly/3SkMohx"><img src="./assets/SG_img/colab_logo.png" /> Detection Transfer Learning</a>
+   </td>
+ <td width="200">   
+<a target="_blank" href="https://bit.ly/3DF8siG"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
 </table>
  </br></br>
-  -->
- 
-## Transfer Learning
-### Transfer Learning with SG Notebook - Semantic Segmentation
-Learn more about SuperGradients transfer learning or fine tuning abilities with our Citiscapes pre-trained RegSeg48 fine tuning into a sub-dataset of Supervisely example notebook on Google Colab for an easy to use tutorial using free GPU hardware
 
-<table class="tfo-notebook-buttons" align="left">
- <td>
-   <a target="_blank" href="https://bit.ly/37P04PN"><img src="./docs/assets/SG_img/colab_logo.png" />Segmentation Transfer Learning in Google Colab</a>
- </td>
-  <td>
-   <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_transfer_learning_semantic_segmentation.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
+#### How to Connect Custom Dataset 
+  <table class="tfo-notebook-buttons" align="left">
+ <td width="500">  
+  <a target="_blank" href="https://bit.ly/3dqDlg3"><img src="./assets/SG_img/colab_logo.png" /> Detection How to Connect Custom Dataset</a>
+  </td>
+ <td width="200">      
+<a target="_blank" href="https://bit.ly/3xBlcmq"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
- <td>
-   <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
+</table>
+ </br></br>
+
+
+
+### How to Predict Using Pre-trained Model
+
+#### Segmentation, Detection and Classification Prediction 
+  <table class="tfo-notebook-buttons" align="left">
+ <td width="500">    
+<a target="_blank" href="https://bit.ly/3f4mssd"><img src="./assets/SG_img/colab_logo.png" /> How to Predict Using Pre-trained Model</a>
+  </td>
+ <td width="200">   
+<a target="_blank" href="https://bit.ly/3Sf59Tr"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
 </table>
  </br></br>
 
-## Knowledge Distillation Training
-### Knowledge Distillation Training Quick Start with SG Notebook - ResNet18 example
+
+## Advanced Features
+__________________________________________________________________________________________________________
+### Knowledge Distillation Training
 Knowledge Distillation is a training technique that uses a large model, teacher model, to improve the performance of a smaller model, the student model.
 Learn more about SuperGradients knowledge distillation training with our pre-trained BEiT base teacher model and Resnet18 student model on CIFAR10 example notebook on Google Colab for an easy to use tutorial using free GPU hardware
-
-<table class="tfo-notebook-buttons" align="left">
- <td>
-   <a target="_blank" href="https://bit.ly/3HQvbsg"><img src="./docs/assets/SG_img/colab_logo.png" />KD Training in Google Colab</a>
- </td>
-  <td>
-   <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_knowledge_distillation_quickstart.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
+  <table class="tfo-notebook-buttons" align="left">
+ <td width="500">   
+   <a target="_blank" href="https://bit.ly/3BLA5oR"><img src="./assets/SG_img/colab_logo.png" /> Knowledge Distillation Training</a>
+  </td>
+ <td width="200">   
+<a target="_blank" href="https://bit.ly/3S9UlG4"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
- <td>
-   <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
+</table>
+ </br></br>
+
+### Recipes
+To train a model, it is necessary to configure 4 main components. These components are aggregated into a single "main" recipe `.yaml` file that inherits the aforementioned dataset, architecture, raining and checkpoint params. It is also possible (and recomended for flexibility) to override default settings with custom ones.
+All recipes can be found [here](src/super_gradients/recipes/Training_Recipes.md)
+  <table class="tfo-notebook-buttons" align="left">
+ <td width="500">   
+   <a target="_blank" href="https://bit.ly/3UiY5ab"><img src="./assets/SG_img/colab_logo.png" /> How to Use Recipes</a>
+  </td>
+ <td width="200">  
+<a target="_blank" href="https://bit.ly/3QSrHbm"><img src="./assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
 </table>
  </br></br>
 
+
+### Using DDP
+```python
+from super_gradients import init_trainer
+from super_gradients.common import MultiGPUMode
+from super_gradients.training.utils.distributed_training_utils import setup_gpu_mode
+
+# Initialize the environment
+init_trainer()
+
+# Launch DDP on 1 device (node) of 4 GPU's
+setup_gpu_mode(gpu_mode=MultiGPUMode.DISTRIBUTED_DATA_PARALLEL, num_gpus=4)
+
+# Define the objects
+
+# The trainer will run on DDP without anything else to change
+```
+### Easily change architectures parameters
+```python
+from super_gradients.training import models
+
+# instantiate default pretrained resnet18
+default_resnet18 = models.get(name="resnet18", num_classes=100, pretrained_weights="imagenet")
+
+# instantiate pretrained resnet18, turning DropPath on with probability 0.5
+droppath_resnet18 = models.get(name="resnet18", arch_params={"droppath_prob": 0.5}, num_classes=100, pretrained_weights="imagenet")
+
+# instantiate pretrained resnet18, without classifier head. Output will be from the last stage before global pooling
+backbone_resnet18 = models.get(name="resnet18", arch_params={"backbone_mode": True}, pretrained_weights="imagenet")
+```
+
+### Using phase callbacks
+```python
+from super_gradients import Trainer
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+from super_gradients.training.utils.callbacks import Phase, LRSchedulerCallback
+from super_gradients.training.metrics.classification_metrics import Accuracy
+
+# define PyTorch train and validation loaders and optimizer
+
+# define what to be called in the callback
+rop_lr_scheduler = ReduceLROnPlateau(optimizer, mode="max", patience=10, verbose=True)
+
+# define phase callbacks, they will fire as defined in Phase
+phase_callbacks = [LRSchedulerCallback(scheduler=rop_lr_scheduler,
+                                       phase=Phase.VALIDATION_EPOCH_END,
+                                       metric_name="Accuracy")]
+
+# create a trainer object, look the declaration for more parameters
+trainer = Trainer("experiment_name")
+
+# define phase_callbacks as part of the training parameters
+train_params = {"phase_callbacks": phase_callbacks}
+```
+### Integration to Weights and Biases
+```python
+from super_gradients import Trainer
+
+# create a trainer object, look the declaration for more parameters
+trainer = Trainer("experiment_name")
+
+train_params = { ... # training parameters
+                "sg_logger": "wandb_sg_logger", # Weights&Biases Logger, see class WandBSGLogger for details
+                "sg_logger_params": # paramenters that will be passes to __init__ of the logger 
+                  {
+                    "project_name": "project_name", # W&B project name
+                    "save_checkpoints_remote": True
+                    "save_tensorboard_remote": True
+                    "save_logs_remote": True
+                  } 
+               }
+```
+
+
 ## Installation Methods
+__________________________________________________________________________________________________________
 ### Prerequisites
 <details>
   
@@ -339,117 +415,53 @@ pip install git+https://github.com/Deci-AI/super-gradients.git@stable
 </details> 
 
 
-## Computer Vision Models - Pretrained Checkpoints
-
-### Pretrained Classification PyTorch Checkpoints
-
-
-| Model | Dataset |  Resolution |    Top-1    |    Top-5   | Latency (HW)*<sub>T4</sub>  | Latency (Production)**<sub>T4</sub> |Latency (HW)*<sub>Jetson Xavier NX</sub>  | Latency (Production)**<sub>Jetson Xavier NX</sub> | Latency <sub>Cascade Lake</sub>  |
-|------------ | ------ | ---------- |----------- | ----------- | ----------- |---------- |----------- | ----------- | :------: |
-| ViT base | ImageNet21K | 224x224 |  84.15  | - |**4.46ms** |**4.60ms** | **-** * |**-**|**57.22ms** |
-| ViT large | ImageNet21K | 224x224 |  85.64  | - |**12.81ms** |**13.19ms** | **-** * |**-**|**187.22ms** |
-| BEiT | ImageNet21K | 224x224 |  -  | - |**-ms** |**-ms** | **-** * |**-**|**-ms** |
-| EfficientNet B0 | ImageNet | 224x224 |  77.62  | 93.49 |**0.93ms** |**1.38ms** | **-** * |**-**|**3.44ms** |
-| RegNet Y200 | ImageNet  |224x224 |  70.88   | 89.35 |**0.63ms** | **1.08ms** | **2.16ms** |**2.47ms**|**2.06ms** |
-| RegNet Y400  | ImageNet |224x224 |  74.74   | 91.46 |**0.80ms** | **1.25ms** |**2.62ms** |**2.91ms** |**2.87ms** |
-| RegNet Y600  | ImageNet |224x224 |  76.18   | 92.34 |**0.77ms** | **1.22ms** |**2.64ms** |**2.93ms** |**2.39ms** |
-| RegNet Y800  | ImageNet |224x224 |  77.07  |  93.26 |**0.74ms** | **1.19ms** |**2.77ms** |**3.04ms** |**2.81ms** |
-| ResNet 18   | ImageNet  |224x224   |  70.6   |   89.64 |**0.52ms** | **0.95ms** |**2.01ms**|**2.30ms** |**4.56ms** |
-| ResNet 34  | ImageNet  |224x224   |  74.13   |   91.7  |**0.92ms**  |**1.34ms** |**3.57ms**|**3.87ms** | **7.64ms** |
-| ResNet 50  | ImageNet  |224x224   |  81.91  |   93.0  |**1.03ms** | **1.44ms** | **4.78ms**|**5.10ms** |**9.25ms** |
-| MobileNet V3_large-150 epochs | ImageNet  |224x224   |  73.79    |   91.54  |**0.67ms** | **1.11ms** |**2.42ms** |**2.71ms** |**1.76ms** |
-| MobileNet V3_large-300 epochs  | ImageNet  |224x224   |  74.52    |  91.92 |**0.67ms** | **1.11ms** |**2.42ms** |**2.71ms** |**1.76ms** |
-| MobileNet V3_small | ImageNet  |224x224   |67.45    |  87.47   |**0.55ms** | **0.96ms** |**2.01ms** *|**2.35ms** |**1.06ms** |
-| MobileNet V2_w1   | ImageNet  |224x224   |  73.08 | 91.1  |**0.46 ms**| **0.89ms** |**1.65ms** *|**1.90ms** | **1.56ms** |
-> **NOTE:** <br/>
-> - Latency (HW)* - Hardware performance (not including IO)<br/>
-> - Latency (Production)** - Production Performance (including IO)
-> - Performance measured for T4 and Jetson Xavier NX with TensorRT, using FP16 precision and batch size 1
-> - Performance measured for Cascade Lake CPU with OpenVINO, using FP16 precision and batch size 1
-
-
-
-### Pretrained Object Detection PyTorch Checkpoints
-
-
-| Model | Dataset |  Resolution | mAP<sup>val<br>0.5:0.95 | Latency (HW)*<sub>T4</sub>  | Latency (Production)**<sub>T4</sub> |Latency (HW)*<sub>Jetson Xavier NX</sub>  | Latency (Production)**<sub>Jetson Xavier NX</sub> | Latency <sub>Cascade Lake</sub>  |
-|------------- |------ | ---------- |------ | -------- |------ | ---------- |------ | :------: |
-| SSD lite MobileNet v2 | COCO |320x320 |21.5 |**0.77ms** |**1.40ms**|**5.28ms** |**6.44ms** |**4.13ms**|
-| SSD lite MobileNet v1 | COCO |320x320 |24.3 |**1.55ms** |**2.84ms**|**8.07ms** |**9.14ms** |**22.76ms**|
-| YOLOX nano | COCO |640x640 |26.77|**2.47ms** |**4.09ms**|**11.49ms** |**12.97ms** |**-**|
-| YOLOX tiny | COCO |640x640 |37.18|**3.16ms** |**4.61ms**|**15.23ms** |**19.24ms** |**-**|
-| YOLOX small | COCO |640x640 |40.47 |**3.58ms** |**4.94ms**|**18.88ms** |**22.48ms** |**-**|
-| YOLOX medium| COCO |640x640 |46.4 |**6.40ms** |**7.65ms**|**39.22ms** |**44.5ms** |**-**|
-| YOLOX large | COCO |640x640 |49.25 |**10.07ms** |**11.12ms**|**68.73ms** |**77.01ms** |**-**|
-  
-
-> **NOTE:** <br/>
-> - Latency (HW)* - Hardware performance (not including IO)<br/>
-> - Latency (Production)** - Production Performance (including IO)
-> - Latency performance measured for T4 and Jetson Xavier NX with TensorRT, using FP16 precision and batch size 1
-> - Latency performance measured for Cascade Lake CPU with OpenVINO, using FP16 precision and batch size 1
-
-### Pretrained Semantic Segmentation PyTorch Checkpoints
-
-
-| Model | Dataset |  Resolution | mIoU | Latency b1<sub>T4</sub> | Latency b1<sub>T4</sub> including IO |
-|--------------------- |------ | ---------- | ------ | -------- | :------: |
-| DDRNet 23   | Cityscapes |1024x2048   |80.26 |**7.62ms** |**25.94ms**|
-| DDRNet 23 slim   | Cityscapes |1024x2048 |78.01 |**3.56ms** |**22.80ms**|
-| STDC 1-Seg50   | Cityscapes | 512x1024 |75.07 |**2.83ms** |**12.57ms**|
-| STDC 1-Seg75   | Cityscapes | 768x1536 |77.8  |**5.71ms** |**26.70ms**|
-| STDC 2-Seg50   | Cityscapes | 512x1024 |75.79 |**3.74ms** |**13.89ms**
-| STDC 2-Seg75   | Cityscapes | 768x1536 |78.93 |**7.35ms** |**28.18ms**|
-| RegSeg (exp48)   | Cityscapes | 1024x2048 |78.15 |**13.09ms** |**41.88ms**|
-| Larger RegSeg (exp53)   | Cityscapes | 1024x2048 |79.2|**24.82ms** |**51.87ms**|
-| ShelfNet LW 34 | COCO Segmentation (21 classes from PASCAL including background) |512x512 |65.1  |**-** |**-** |
-
+## Implemented Model Architectures 
+__________________________________________________________________________________________________________
 
-> **NOTE:** Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1 (latency), and not including IO
+Detailed list can be found [here](src/super_gradients/training/models/Implemented%20Model%20Architectures.md) 
 
-## Implemented Model Architectures 
-  
 ### Image Classification
   
-- [DensNet (Densely Connected Convolutional Networks)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/densenet.py) - Densely Connected Convolutional Networks [https://arxiv.org/pdf/1608.06993.pdf](https://arxiv.org/pdf/1608.06993.pdf)
-- [DPN](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/dpn.py) - Dual Path Networks [https://arxiv.org/pdf/1707.01629](https://arxiv.org/pdf/1707.01629)
-- [EfficientNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/efficientnet.py) - [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
-- [GoogleNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/googlenet.py) - [https://arxiv.org/pdf/1409.4842](https://arxiv.org/pdf/1409.4842)
-- [LeNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/lenet.py) - [https://yann.lecun.com/exdb/lenet/](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf)
-- [MobileNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenet.py) - Efficient Convolutional Neural Networks for Mobile Vision Applications [https://arxiv.org/pdf/1704.04861](https://arxiv.org/pdf/1704.04861)
-- [MobileNet v2](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenetv2.py) - [https://arxiv.org/pdf/1801.04381](https://arxiv.org/pdf/1801.04381) 
-- [MobileNet v3](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenetv3.py) - [https://arxiv.org/pdf/1905.02244](https://arxiv.org/pdf/1905.02244)
-- [PNASNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/pnasnet.py) - Progressive Neural Architecture Search Networks [https://arxiv.org/pdf/1712.00559](https://arxiv.org/pdf/1712.00559)
-- [Pre-activation ResNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/preact_resnet.py) - [https://arxiv.org/pdf/1603.05027](https://arxiv.org/pdf/1603.05027)  
-- [RegNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/regnet.py) - [https://arxiv.org/pdf/2003.13678.pdf](https://arxiv.org/pdf/2003.13678.pdf) 
-- [RepVGG](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/repvgg.py) - Making VGG-style ConvNets Great Again [https://arxiv.org/pdf/2101.03697.pdf](https://arxiv.org/pdf/2101.03697.pdf) 
-- [ResNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/resnet.py) - Deep Residual Learning for Image Recognition [https://arxiv.org/pdf/1512.03385](https://arxiv.org/pdf/1512.03385)  
-- [ResNeXt](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/resnext.py) - Aggregated Residual Transformations for Deep Neural Networks [https://arxiv.org/pdf/1611.05431](https://arxiv.org/pdf/1611.05431)
-- [SENet ](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/senet.py) - Squeeze-and-Excitation Networks[https://arxiv.org/pdf/1709.01507](https://arxiv.org/pdf/1709.01507)
-- [ShuffleNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/shufflenet.py) - [https://arxiv.org/pdf/1707.01083](https://arxiv.org/pdf/1707.01083)
-- [ShuffleNet v2](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/shufflenetv2.py) - Efficient Convolutional Neural Network for Mobile
-Devices[https://arxiv.org/pdf/1807.11164](https://arxiv.org/pdf/1807.11164)
-- [VGG](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/vgg.py) - Very Deep Convolutional Networks for Large-scale Image Recognition [https://arxiv.org/pdf/1409.1556](https://arxiv.org/pdf/1409.1556)
+- [DensNet (Densely Connected Convolutional Networks)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/densenet.py) 
+- [DPN](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/dpn.py) 
+- [EfficientNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/efficientnet.py)
+- [LeNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/lenet.py) 
+- [MobileNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenet.py)
+- [MobileNet v2](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenetv2.py)  
+- [MobileNet v3](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenetv3.py) 
+- [PNASNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/pnasnet.py) 
+- [Pre-activation ResNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/preact_resnet.py)  
+- [RegNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/regnet.py)
+- [RepVGG](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/repvgg.py)  
+- [ResNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/resnet.py)
+- [ResNeXt](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/resnext.py) 
+- [SENet ](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/senet.py)
+- [ShuffleNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/shufflenet.py)
+- [ShuffleNet v2](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/shufflenetv2.py)
+- [VGG](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/vgg.py)
   
+### Semantic Segmentation 
+
+- [PP-LiteSeg](https://bit.ly/3RrtMMO)
+- [DDRNet (Deep Dual-resolution Networks)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/ddrnet.py) 
+- [LadderNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/laddernet.py)
+- [RegSeg](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/regseg.py)
+- [ShelfNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/shelfnet.py) 
+- [STDC](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/stdc.py)
   
+
 ### Object Detection
   
 - [CSP DarkNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/csp_darknet53.py)
 - [DarkNet-53](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/darknet53.py)
-- [SSD (Single Shot Detector)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/ssd.py) - [https://arxiv.org/pdf/1512.02325](https://arxiv.org/pdf/1512.02325)
-- [YOLOX](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/yolox.py) - [https://arxiv.org/abs/2107.08430](https://arxiv.org/abs/2107.08430)
+- [SSD (Single Shot Detector)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/ssd.py) 
+- [YOLOX](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/yolox.py)
   
   
-### Semantic Segmentation 
-  
-- [DDRNet (Deep Dual-resolution Networks)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/ddrnet.py) - [https://arxiv.org/pdf/2101.06085.pdf](https://arxiv.org/pdf/2101.06085.pdf)
-- [LadderNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/laddernet.py) - Multi-path networks based on U-Net for medical image segmentation [https://arxiv.org/pdf/1810.07810](https://arxiv.org/pdf/1810.07810)
-- [RegSeg](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/regseg.py) - Rethink Dilated Convolution for Real-time Semantic Segmentation [https://arxiv.org/pdf/2111.09957](https://arxiv.org/pdf/2111.09957)
-- [ShelfNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/shelfnet.py) - [https://arxiv.org/pdf/1811.11254](https://arxiv.org/pdf/1811.11254)
-- [STDC](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/stdc.py) - Rethinking BiSeNet For Real-time Semantic Segmentation [https://arxiv.org/pdf/2104.13188](https://arxiv.org/pdf/2104.13188)
-  
-</details>
-  
+
+__________________________________________________________________________________________________________
+
+
 ## Documentation
 
 Check SuperGradients [Docs](https://deci-ai.github.io/super-gradients/welcome.html) for full documentation, user guide, and examples.
@@ -499,7 +511,7 @@ ________________________________________________________________________________
 
 Deci Platform is our end to end platform for building, optimizing and deploying deep learning models to production.
 
-Sign up for our [FREE Community Tier](https://console.deci.ai/) to enjoy immediate improvement in throughput, latency, memory footprint and model size.
+[Request free trial](https://bit.ly/3qO3icq) to enjoy immediate improvement in throughput, latency, memory footprint and model size.
 
 Features:
 - Automatically compile and quantize your models with just a few clicks (TensorRT, OpenVINO).
@@ -507,6 +519,6 @@ Features:
 - Easily benchmark your models’ performance on different hardware and batch sizes.
 - Invite co-workers to collaborate on models and communicate your progress.
 - Deci supports all common frameworks and Hardware, from Intel CPUs to Nvidia's GPUs and Jetsons.
+ֿ
 
-Sign up for Deci Platform for free [here](https://console.deci.ai/) 
-
+Request free trial [here](https://bit.ly/3qO3icq)