@@ -1,4 +1,5 @@
 
+# Model Zoo
 ## Computer Vision Models - Pretrained Checkpoints
 
 You can load any of our pretrained model in 2 lines of code:
@@ -84,3 +85,59 @@ All the available models are listed in the column `Model name`.
 > **NOTE:** For resolutions below 1024x2048 we first resize the input to the inference resolution and then resize the predictions to 1024x2048. The time of resizing is included in the measurements so that the practical input-size is 1024x2048.
  
 > **NOTE:** DDRNet23 and DDRNet23_Slim results were achieved with channel wise knowledge distillation training recipe.
+
+### Pretrained Pose Estimation PyTorch Checkpoints
+
+| Model           | Model Name      | Dataset     | Resolution | AP  | Latency b1<sub>T4</sub> | Latency b1<sub>T4</sub> including IO | Latency (Production)**<sub>Jetson Xavier NX</sub> | 
+|-----------------|-----------------|-------------|------------|-----|-------------------------|--------------------------------------|:-------------------------------------------------:|
+| DEKR_W32_NO_DC  | dekr_w32_no_dc  | COCO2017 PE | 640x640    |     |                         |                                      |                                                   |
+| POSE_PP_YOLO_L  | pose_ppyolo_l   | COCO2017 PE | 640x640    |     |                         |                                      |                                                   |
+| POSE_DDRNET_39  | pose_ddrnet39   | COCO2017 PE | 640x640    |     |                         |                                      |                                                   |
+
+
+## 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)
+  
+  
+### 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)
+- [PP-YoloE](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py) - [https://arxiv.org/abs/2203.16250](https://arxiv.org/abs/2203.16250)
+
+### Semantic Segmentation 
+
+- [PP-LiteSeg](https://bit.ly/3RrtMMO) - [https://arxiv.org/pdf/2204.02681v1.pdf](https://arxiv.org/pdf/2204.02681v1.pdf) 
+- [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)
+  
+
+### Pose Estimation
+- [HRNet DEKR](https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation) - Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression [https://arxiv.org/pdf/2104.02300.pdf](https://arxiv.org/pdf/2104.02300.pdf)
+</details>