@@ -48,7 +48,7 @@ ________________________________________________________________________________
 # Load model with pretrained weights
 model = models.get("yolox_s", pretrained_weights="coco")
 ```
-#### All Computer Vision Models - Pretrained Checkpoints can be found [here](src/super_gradients/training/Computer_Vision_Models_Pretrained_Checkpoints.md)
+#### All Computer Vision Models - Pretrained Checkpoints can be found [here](http://bit.ly/3EGfKD4)
 
 #### Classification
 <div align="center">
@@ -108,20 +108,20 @@ pip install super-gradients
 
 ## What's New
 __________________________________________________________________________________________________________
-* 【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)🎯
+* 【17/11/2022】 Integration with ClearML
+* 【06/9/2022】Supporting PyTorch Datasets and Dataloaders
+* 【06/9/2022】 PP-LiteSeg - new pre-trained [checkpoints](http://bit.ly/3EGfKD4) and [recipes](http://bit.ly/3gfLw07) for Cityscapes with SOTA mIoU scores (~1.5% above paper)🎯
+* 【07/08/2022】DDRNet23 -  new pre-trained [checkpoints](http://bit.ly/3EGfKD4) and [recipes](http://bit.ly/3gfLw07) 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](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)🎯
+* 【07/07/2022】SSD Lite MobileNet V2,V1 - Training [recipes](http://bit.ly/3gfLw07) and pre-trained [checkpoints](http://bit.ly/3EGfKD4) on COCO - Tailored for edge devices! 📱
+* 【07/07/2022】 STDC  - new pre-trained [checkpoints](http://bit.ly/3EGfKD4) and [recipes](http://bit.ly/3gfLw07) 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).
+- [ ] PP-Yolo-E implementation
+- [ ] Tools for faster training 
 - [ ] Integration with more professional tools.
 
 
@@ -153,7 +153,7 @@ The most simple and straightforward way to start training SOTA performance model
 python -m super_gradients.examples.train_from_recipe_example.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 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)
+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](http://bit.ly/3EGfKD4)
 
 ```python
 # The pretrained_weights argument will load a pre-trained architecture on the provided dataset
@@ -276,7 +276,10 @@ Learn more about SuperGradients knowledge distillation training with our pre-tra
 
 ### 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)
+All recipes can be found [here](http://bit.ly/3gfLw07)
+</br>
+Recipes support out of the box every model, metric or loss that is implemented in SuperGradients, but you can easily extend this to any custom object that you need by "registering it". Check out [this](http://bit.ly/3TQ4iZB) tutorial for more information.
+
   <table class="tfo-notebook-buttons" align="left">
  <td width="500">   
    <a target="_blank" href="https://bit.ly/3UiY5ab"><img src="./docs/assets/SG_img/colab_logo.png" /> How to Use Recipes</a>
@@ -434,7 +437,7 @@ pip install git+https://github.com/Deci-AI/super-gradients.git@stable
 ## Implemented Model Architectures 
 __________________________________________________________________________________________________________
 
-Detailed list can be found [here](src/super_gradients/training/models/Implemented%20Model%20Architectures.md) 
+Detailed list can be found [here](http://bit.ly/3GnJwgZ) 
 
 ### Image Classification