@@ -120,26 +120,27 @@ ________________________________________________________________________________
 pip install super-gradients
 ```
 
-## What's New - Version 3.0.8
+## What's New - Version 3.1.0 (May 3rd)
 __________________________________________________________________________________________________________
-*  [QAT&PTQ](https://bit.ly/41hC8uI)
-* [Pose estimation](http://bit.ly/3o0xHq2)
-* [New documentation](http://bit.ly/3KYVCiJ)
-* [New semantic segmentation dataset - Mapillary Vistas Dataset](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/datasets/segmentation_datasets/mapillary_dataset.py)
-*  Lion optimizer
-* PP-YoloE pre-trained - new pre-trained [checkpoints](https://bit.ly/41dkt89) and [recipes](http://bit.ly/3gfLw07) for COCO2017 🎯
-* DDRNet pre-trained segmentation model - new pre-trained [checkpoints](https://bit.ly/41dkt89) and [recipes](http://bit.ly/3gfLw07) for Cityscapes and  [Knowledge distillation recipe for DDRNet](http://bit.ly/3GzZHHo)🎯
-
+* [YOLO-NAS](https://bit.ly/41WeNPZ)
+* New [predict function](https://bit.ly/3oZfaea) (predict on any image, video, url, path, stream)
+* [RoboFlow100](https://bit.ly/40YOJ5z) datasets integration 
+* A new [Documentation Hub](https://docs.deci.ai/super-gradients/documentation/source/welcome.html)
+* Integration with [DagsHub for experiment monitoring](https://bit.ly/3ALFUkQ)
+* Support [Darknet/Yolo format detection dataset](https://bit.ly/41VX6Qu) (used by Yolo v5, v6, v7, v8) 
+* [Segformer](https://bit.ly/3oYu6Jp) model and recipe 
+* Post Training Quantization and Quantization Aware Training - [notebooks](http://bit.ly/3KrN6an)
 
 Check out SG full [release notes](https://github.com/Deci-AI/super-gradients/releases).
 
 ## Coming soon
 __________________________________________________________________________________________________________
 - [ ] Pre-trained pose estimation model
-- [ ] New predict function on detection models
-- [ ] RoboFlow100 datasets integration 
-- [ ] A new documentation hub
+- [ ] Test Time Augmentations (TTA)
+- [ ] Recipe to train DEKR model(convertable to TRT) 
+- [ ] Key-points Rescoring for Pose estimation 
 - [ ] LR finder
+- [ ] Data analysis tools
 
 
 ## Table of Content

@@ -44,19 +44,22 @@ All the available models are listed in the column `Model name`.
 ### Pretrained Object Detection PyTorch Checkpoints
 
 
-| Model                 | Model Name            | 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 | ssd_lite_mobilenet_v2 | COCO    | 320x320    | 21.5                    | **0.77ms**                 | **1.40ms**                          | **5.28ms**                               | **6.44ms**                                        |            **4.13ms**           |
-| SSD lite MobileNet v1 | ssd_mobilenet_v1      | COCO    | 320x320    | 24.3                    | **1.55ms**                 | **2.84ms**                          | **8.07ms**                               | **9.14ms**                                        |           **22.76ms**           |
-| YOLOX nano            | yolox_n               | COCO    | 640x640    | 26.77                   | **2.47ms**                 | **4.09ms**                          | **11.49ms**                              | **12.97ms**                                       |              **-**              |
-| YOLOX tiny            | yolox_t               | COCO    | 640x640    | 37.18                   | **3.16ms**                 | **4.61ms**                          | **15.23ms**                              | **19.24ms**                                       |              **-**              |
-| YOLOX small           | yolox_s               | COCO    | 640x640    | 40.47                   | **3.58ms**                 | **4.94ms**                          | **18.88ms**                              | **22.48ms**                                       |              **-**              |
-| YOLOX medium          | yolox_m               | COCO    | 640x640    | 46.4                    | **6.40ms**                 | **7.65ms**                          | **39.22ms**                              | **44.5ms**                                        |              **-**              |
-| YOLOX large           | yolox_l               | COCO    | 640x640    | 49.25                   | **10.07ms**                | **11.12ms**                         | **68.73ms**                              | **77.01ms**                                       |              **-**              |
-| PP-YOLOE small        | ppyoloe_s            | COCO    | 640x640    | 42.52                   | **2.39ms**                 | **4.3ms**                           | **14.28ms**                              | **14.99ms**                                       |              **-**              |
-| PP-YOLOE medium       | ppyoloe_m            | COCO    | 640x640    | 47.11                   | **5.16ms**                 | **7.05ms**                          | **32.71ms**                              | **33.46ms**                                       |              **-**              |
-| PP-YOLOE large        | ppyoloe_l            | COCO    | 640x640    | 49.48                   | **7.65ms**                 | **9.59ms**                          | **51.13ms**                              | **50.39ms**                                       |              **-**              |
-| PP-YOLOE x-large      | ppyoloe_x            | COCO    | 640x640    | 51.15                   | **14.04ms**                | **15.96ms**                         | **94.92ms**                              | **94.22ms**                                       |              **-**              |
+| Model               | Model Name            | 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> |
+|---------------------|-----------------------|---------|------------|-------------------------|-------------------------------|-------------------------------------|------------------------------------------|---------------------------------------------------|:-------------------------------:|
+ | YOLO-NAS S	         | yolo_nas_s            | COCO |  640x640| 47.5(FP16) 47.03(INT8)	 | **3.21(FP16)** **2.36(INT8)** |
+ | YOLO-NAS M	         | yolo_nas_m            | COCO |  640x640| 51.55(FP16) 51.0(INT8)   | **5.85(FP16)** **3.78(INT8)** |
+ | YOLO-NAS L	         | yolo_nas_l            | COCO |  640x640| 52.22(FP16) 52.1(INT8)	  | **7.87(FP16)** **4.78(INT8)** |
+| PP-YOLOE small      | ppyoloe_s             | COCO    | 640x640    | 42.52                   | **2.39ms**                    | **4.3ms**                           | **14.28ms**                              | **14.99ms**                                       |              **-**              |
+| PP-YOLOE medium     | ppyoloe_m             | COCO    | 640x640    | 47.11                   | **5.16ms**                    | **7.05ms**                          | **32.71ms**                              | **33.46ms**                                       |              **-**              |
+| PP-YOLOE large      | ppyoloe_l             | COCO    | 640x640    | 49.48                   | **7.65ms**                    | **9.59ms**                          | **51.13ms**                              | **50.39ms**                                       |              **-**              |
+| PP-YOLOE x-large    | ppyoloe_x             | COCO    | 640x640    | 51.15                   | **14.04ms**                   | **15.96ms**                         | **94.92ms**                              | **94.22ms**                                       |              **-**              |
+| YOLOX nano          | yolox_n               | COCO    | 640x640    | 26.77                   | **2.47ms**                    | **4.09ms**                          | **11.49ms**                              | **12.97ms**                                       |              **-**              |
+| YOLOX tiny          | yolox_t               | COCO    | 640x640    | 37.18                   | **3.16ms**                    | **4.61ms**                          | **15.23ms**                              | **19.24ms**                                       |              **-**              |
+| YOLOX small         | yolox_s               | COCO    | 640x640    | 40.47                   | **3.58ms**                    | **4.94ms**                          | **18.88ms**                              | **22.48ms**                                       |              **-**              |
+| YOLOX medium        | yolox_m               | COCO    | 640x640    | 46.4                    | **6.40ms**                    | **7.65ms**                          | **39.22ms**                              | **44.5ms**                                        |              **-**              |
+| YOLOX large         | yolox_l               | COCO    | 640x640    | 49.25                   | **10.07ms**                   | **11.12ms**                         | **68.73ms**                              | **77.01ms**                                       |              **-**              |
+| SSD lite MobileNet v2 | ssd_lite_mobilenet_v2 | COCO    | 320x320    | 21.5                    | **0.77ms**                    | **1.40ms**                          | **5.28ms**                               | **6.44ms**                                        |            **4.13ms**           |
+| SSD lite MobileNet v1 | ssd_mobilenet_v1      | COCO    | 320x320    | 24.3                    | **1.55ms**                    | **2.84ms**                          | **8.07ms**                               | **9.14ms**                                        |           **22.76ms**           |
 
 > **NOTE:** <br/>
 > - Latency (HW)* - Hardware performance (not including IO)<br/>

@@ -32,28 +32,29 @@ Check out our [Quickstart tutorial](QuickstartBasicToolkit.md) to get learn the
 
 You can also start from our tutorial on [Detection](ObjectDetection.md), [Segmentation](Segmentation.md) or [Pose Estimation](PoseEstimation.md).
  
-## What's New
+## What's New (v3.1.0)
 __________________________________________________________________________________________________________
-* 【1/3/2023】 Lion optimizer was added  
-* 【27/2/2023】 Pose Estimation models and utilities were added to SuperGradients! 
-* 【20/2/2023】 PP-Yolo-E implementation 
-* 【17/1/2023】 Quantization Aware Training (QAT) and Post Training Quantization (PTQ) - including selective quantization 
-* 【17/11/2022】 Integration with ClearML
-* 【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](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)🎯
+
+* [YOLO-NAS](https://bit.ly/41WeNPZ)
+* New [predict function](https://bit.ly/3oZfaea) (predict on any image, video, url, path, stream)
+* [RoboFlow100](https://bit.ly/40YOJ5z) datasets integration 
+* A new [Documentation Hub](https://docs.deci.ai/super-gradients/documentation/source/welcome.html)
+* Integration with [DagsHub for experiment monitoring](https://bit.ly/3ALFUkQ)
+* Support [Darknet/Yolo format detection dataset](https://bit.ly/41VX6Qu) (used by Yolo v5, v6, v7, v8) 
+* [Segformer](https://bit.ly/3oYu6Jp) model and recipe 
+* Post Training Quantization and Quantization Aware Training - [notebooks](http://bit.ly/3KrN6an)
 
 Check out SG full [release notes](https://github.com/Deci-AI/super-gradients/releases).
 
 ## Coming soon
 __________________________________________________________________________________________________________
 
-- [ ] Tools for faster training 
-- [ ] Tools for training health monitoring 
-- [ ] Integration with more professional 3rd party tools.
-- [ ] SegFormers
+- [ ] Pre-trained pose estimation model
+- [ ] Test Time Augmentations (TTA)
+- [ ] Recipe to train DEKR model(convertable to TRT) 
+- [ ] Key-points Rescoring for Pose estimation 
+- [ ] LR finder
+- [ ] Data analysis tools
 ## Citation
 
 If you are using SuperGradients library in your research, please cite SuperGradients deep learning training library.