@@ -107,6 +107,7 @@ ________________________________________________________________________________
   - [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)
+- [Training Recipes](#Training-Recipes)
 - [Contributing](#contributing)
 - [Citation](#citation)
 - [Community](#community)
@@ -449,7 +450,218 @@ Devices[https://arxiv.org/pdf/1807.11164](https://arxiv.org/pdf/1807.11164)
 - [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>
-  
+
+## Training Recipes
+
+We defined recipes to ensure that anyone can reproduce our results in the most simple way.
+
+
+**Setup**
+
+To run recipes you first need to clone the super-gradients repository:
+```
+git clone https://github.com/Deci-AI/super-gradients
+```
+
+You then need to move to the root of the clone project (where you find "requirements.txt" and "setup.py") and install super-gradients:
+```
+pip install -e .
+```
+
+Finally, append super-gradients to the python path: (Replace "YOUR-LOCAL-PATH" with the path to the downloaded repo)
+```
+export PYTHONPATH=$PYTHONPATH:<YOUR-LOCAL-PATH>/super-gradients/
+```
+
+
+**How to run a recipe**
+
+The recipes are defined in .yaml format and we use the hydra library to allow you to easily customize the parameters.
+The basic basic syntax is as follow:
+```
+python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=<CONFIG-NAME> dataset_params.data_dir=<PATH-TO-DATASET>
+```
+But in most cases you will want to train on multiple GPU's using this syntax:
+```
+python -m torch.distributed.launch --nproc_per_node=<N-NODES> src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=<CONFIG-NAME> dataset_params.data_dir=<PATH-TO-DATASET>
+```
+*Note: this script needs to be launched from the root folder of super_gradients*
+*Note: if you stored your dataset in the path specified by the recipe you can drop "dataset_params.data_dir=<PATH-TO-DATASET>".*
+
+**Explore our recipes**
+
+You can find all of our recipes [here](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes).
+You will find information about the performance of a recipe as well as the command to execute it in the header of its config file.
+
+*Example: [Training of YoloX Small on Coco 2017](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes/coco2017_yolox.yaml), using 8 GPU* 
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_s dataset_params.data_dir=/home/coco2017
+```
+
+
+
+**List of commands**
+
+All the commands to launch the recipes described [here](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes) are listed below.
+Please make to "dataset_params.data_dir=<PATH-TO-DATASET>" if you did not store the dataset in the path specified by the recipe (as showed in the example above).
+
+**- Classification**
+<details>
+<summary>Cifar10</summary>
+
+resnet:
+```
+python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cifar10_resnet +experiment_name=cifar10
+```
+
+</details>
+<details>
+<summary>ImageNet</summary>
+
+efficientnet
+```
+python -m torch.distributed.launch --nproc_per_node=4 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_efficientnet
+```
+mobilenetv2
+```
+python -m torch.distributed.launch --nproc_per_node=2 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv2
+```
+mobilenetv3 small
+```
+python -m torch.distributed.launch --nproc_per_node=2 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv3_small
+```
+mobilenetv3 large
+```
+python -m torch.distributed.launch --nproc_per_node=2 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv3_large
+```
+regnetY200
+```
+python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY200
+```
+regnetY400
+```
+python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY400
+```
+regnetY600
+```
+python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY600
+```
+regnetY800
+```
+python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY800
+```
+repvgg
+```
+python -m torch.distributed.launch --nproc_per_node=4 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_repvgg
+```
+resnet50
+```
+python -m torch.distributed.launch --nproc_per_node=4 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_resnet50
+```
+resnet50_kd
+```
+python -m torch.distributed.launch --nproc_per_node=8  src/super_gradients/examples/train_from_kd_recipe_example/train_from_kd_recipe.py --config-name=imagenet_resnet50_kd
+```
+vit_base
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_vit_base
+```
+vit_large
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_vit_large
+```
+</details>
+
+**- Detection**
+
+<details>
+<summary>Coco2017</summary>
+
+ssd_lite_mobilenet_v2
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_ssd_lite_mobilenet_v2
+```
+yolox_n
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_n
+```
+yolox_t
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_t
+```
+yolox_s
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_s
+```
+yolox_m
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_m
+```
+yolox_l
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_l
+```
+yolox_x
+```
+python -m torch.distributed.launch --nproc_per_node=8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_x
+```
+
+</details>
+
+
+**- Segmentation**
+
+<details>
+<summary>Cityscapes</summary>
+
+DDRNet23
+```
+python -m torch.distributed.launch --nproc_per_node=4 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_ddrnet
+```
+DDRNet23-Slim
+```
+python -m torch.distributed.launch --nproc_per_node=4 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_ddrnet architecture=ddrnet_23_slim
+```
+RegSeg48
+```
+python -m torch.distributed.launch --nproc_per_node=4 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_regseg48
+```
+STDC1-Seg50
+```
+python -m torch.distributed.launch --nproc_per_node=2 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg50
+```
+STDC2-Seg50
+```
+python -m torch.distributed.launch --nproc_per_node=2 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg50 architecture=stdc2_seg
+```
+STDC1-Seg75
+```
+python -m torch.distributed.launch --nproc_per_node=4 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg75
+```
+STDC2-Seg75
+```
+python -m torch.distributed.launch --nproc_per_node=4 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg75 external_checkpoint_path=<stdc2-backbone-pretrained-path> architecture=stdc2_seg
+```
+
+</details>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 ## Documentation
 
 Check SuperGradients [Docs](https://deci-ai.github.io/super-gradients/welcome.html) for full documentation, user guide, and examples.