Merge branch 'master' into fix_mlflow_unsupport_log

Makefile

@@ -6,3 +6,11 @@ integration_tests:
 
 yolo_nas_integration_tests:
 	python -m unittest tests/integration_tests/yolo_nas_integration_test.py
+
+recipe_accuracy_tests:
+	python3.8 src/super_gradients/examples/convert_recipe_example/convert_recipe_example.py --config-name=cifar10_conversion_params experiment_name=shortened_cifar10_resnet_accuracy_test
+	python3.8 src/super_gradients/train_from_recipe.py --config-name=coco2017_pose_dekr_w32_no_dc experiment_name=shortened_coco2017_pose_dekr_w32_ap_test epochs=1 batch_size=4 val_batch_size=8 training_hyperparams.lr_warmup_steps=0 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=1000 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+	python3.8 src/super_gradients/train_from_recipe.py --config-name=cifar10_resnet               experiment_name=shortened_cifar10_resnet_accuracy_test   epochs=100 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+	python3.8 src/super_gradients/train_from_recipe.py --config-name=coco2017_yolox               experiment_name=shortened_coco2017_yolox_n_map_test      epochs=10  architecture=yolox_n training_hyperparams.loss=yolox_fast_loss training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+	python3.8 src/super_gradients/train_from_recipe.py --config-name=cityscapes_regseg48          experiment_name=shortened_cityscapes_regseg48_iou_test   epochs=10 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+	coverage run --source=super_gradients -m unittest tests/deci_core_recipe_test_suite_runner.py

README.md

@@ -1,5 +1,5 @@
 <div align="center" markdown="1">
-  <img src="docs/assets/SG_img/SG - Horizontal Glow 2.png" width="600"/>
+  <img src="documentation/assets/SG_img/SG - Horizontal Glow 2.png" width="600"/>
  <br/><br/>
 
 **Build, train, and fine-tune production-ready deep learning  SOTA vision models**
@@ -63,17 +63,17 @@ model = models.get(Models.YOLO_NAS_M, pretrained_weights="coco")
 
 #### Classification
 <div align="center">
-<img src="./docs/assets/SG_img/Classification@2xDark.png" width="800px">
+<img src="./documentation/assets/SG_img/Classification@2xDark.png" width="800px">
 </div>
 
 #### Semantic Segmentation
 <div align="center">
-<img src="./docs/assets/SG_img/Semantic Segmentation@2xDark.png" width="800px">
+<img src="./documentation/assets/SG_img/Semantic Segmentation@2xDark.png" width="800px">
 </div>
 
 #### Object Detection 
 <div align="center">
-<img src="./docs/assets/SG_img/Object Detection@2xDark.png" width="800px">
+<img src="./documentation/assets/SG_img/Object Detection@2xDark.png" width="800px">
 </div>
 
 
@@ -192,10 +192,10 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
 #### Transfer Learning 
   <table class="tfo-notebook-buttons" align="left">
  <td width="500">  
-  <a target="_blank" href="https://bit.ly/3xzIutb"><img src="./docs/assets/SG_img/colab_logo.png" /> Classification Transfer Learning</a>
+  <a target="_blank" href="https://bit.ly/3xzIutb"><img src="./documentation/assets/SG_img/colab_logo.png" /> Classification Transfer Learning</a>
   </td>
  <td width="200">    
- <a target="_blank" href="https://bit.ly/3xwYEn1"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
+ <a target="_blank" href="https://bit.ly/3xwYEn1"><img src="./documentation/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
  </td>
 </table>
  </br></br>
@@ -206,7 +206,7 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
 ####  Quick Start 
 <table class="tfo-notebook-buttons" align="left">
  <td width="500">
-<a target="_blank" href="https://bit.ly/3qKx9m8"><img src="./docs/assets/SG_img/colab_logo.png" /> Segmentation Quick Start</a>
+<a target="_blank" href="https://bit.ly/3qKx9m8"><img src="./documentation/assets/SG_img/colab_logo.png" /> Segmentation Quick Start</a>
  </td>
 </table>
  </br></br>
@@ -216,7 +216,7 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
  ####  Transfer Learning 
 <table class="tfo-notebook-buttons" align="left">
  <td width="500">
-<a target="_blank" href="https://bit.ly/3qKwMbe"><img src="./docs/assets/SG_img/colab_logo.png" /> Segmentation Transfer Learning</a>
+<a target="_blank" href="https://bit.ly/3qKwMbe"><img src="./documentation/assets/SG_img/colab_logo.png" /> Segmentation Transfer Learning</a>
  </td>
 </table>
  </br></br>
@@ -226,7 +226,7 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
 ####  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="./docs/assets/SG_img/colab_logo.png" /> Segmentation How to Connect Custom Dataset</a>
+<a target="_blank" href="https://bit.ly/3QQBVJp"><img src="./documentation/assets/SG_img/colab_logo.png" /> Segmentation How to Connect Custom Dataset</a>
    </td>
 </table>
  </br></br>
@@ -239,15 +239,15 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
 #### Transfer Learning
   <table class="tfo-notebook-buttons" align="left">
  <td width="500">   
-<a target="_blank" href="https://bit.ly/3SkMohx"><img src="./docs/assets/SG_img/colab_logo.png" /> Detection Transfer Learning</a>
+<a target="_blank" href="https://bit.ly/3SkMohx"><img src="./documentation/assets/SG_img/colab_logo.png" /> Detection Transfer Learning</a>
    </td>
 </table>
  </br></br>
 
 #### 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="./docs/assets/SG_img/colab_logo.png" /> Detection How to Connect Custom Dataset</a>
+  <a target="_blank" href="https://bit.ly/3dqDlg3"><img src="./documentation/assets/SG_img/colab_logo.png" /> Detection How to Connect Custom Dataset</a>
   </td>
 </table>
  </br></br>
@@ -259,7 +259,7 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
 #### 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="./docs/assets/SG_img/colab_logo.png" /> How to Predict Using Pre-trained Model</a>
+<a target="_blank" href="https://bit.ly/3f4mssd"><img src="./documentation/assets/SG_img/colab_logo.png" /> How to Predict Using Pre-trained Model</a>
   </td>
 </table>
  </br></br>
@@ -271,15 +271,15 @@ ________________________________________________________________________________
 Quantization involves representing weights and biases in lower precision, resulting in reduced memory and computational requirements, making it useful for deploying models on devices with limited resources. The process can be done during training, called Quantization aware training, or after training, called post-training quantization. A full tutorial can be found [here](http://bit.ly/41hC8uI).
   <table class=“tfo-notebook-buttons” align=“left”>
  <td width=“500”>
-   <a target="_blank" href="http://bit.ly/3KrN6an"><img src="./docs/assets/SG_img/colab_logo.png" /> Post Training Quantization and Quantization Aware Training</a>
+   <a target="_blank" href="http://bit.ly/3KrN6an"><img src="./documentation/assets/SG_img/colab_logo.png" /> Post Training Quantization and Quantization Aware Training</a>
   </td>
 </table>
 
 ### Quantization Aware Training YoloNAS on Custom Dataset
 This tutorial provides a comprehensive guide on how to fine-tune a YoloNAS model using a custom dataset. It also demonstrates how to utilize SG's QAT (Quantization-Aware Training) support. Additionally, it offers step-by-step instructions on deploying the model and performing benchmarking.
   <table class=“tfo-notebook-buttons” align=“left”>
  <td width=“500”>
-   <a target="_blank" href="https://bit.ly/3MIKdTy"><img src="./docs/assets/SG_img/colab_logo.png" /> Quantization Aware Training YoloNAS on Custom Dataset</a>
+   <a target="_blank" href="https://bit.ly/3MIKdTy"><img src="./documentation/assets/SG_img/colab_logo.png" /> Quantization Aware Training YoloNAS on Custom Dataset</a>
   </td>
 </table>
 
@@ -288,7 +288,7 @@ Knowledge Distillation is a training technique that uses a large model, teacher
 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 width="500">   
-   <a target="_blank" href="https://bit.ly/3BLA5oR"><img src="./docs/assets/SG_img/colab_logo.png" /> Knowledge Distillation Training</a>
+   <a target="_blank" href="https://bit.ly/3BLA5oR"><img src="./documentation/assets/SG_img/colab_logo.png" /> Knowledge Distillation Training</a>
   </td>
 </table>
  </br></br>
@@ -301,7 +301,7 @@ Recipes support out of the box every model, metric or loss that is implemented i
 
   <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>
+   <a target="_blank" href="https://bit.ly/3UiY5ab"><img src="./documentation/assets/SG_img/colab_logo.png" /> How to Use Recipes</a>
   </td>
 </table>
  </br></br>

YOLONAS.md

@@ -60,13 +60,13 @@ We demonstrate great performance of YOLO-NAS on downstream tasks. When fine-tuni
 <tr>
     <td>   
         <a target="_blank" href="https://bit.ly/yolo-nas-starter-notebook">
-            <img src="./docs/assets/SG_img/colab_logo.png" /> Fine-Tuning Notebook 
+            <img src="./documentation/assets/SG_img/colab_logo.png" /> Fine-Tuning Notebook 
         </a>
     </td>
 </tr><tr>
     <td>   
         <a target="_blank" href="https://bit.ly/3MIKdTy">
-            <img src="./docs/assets/SG_img/colab_logo.png" /> Quantization Aware Training YoloNAS on Custom Dataset Notebook
+            <img src="./documentation/assets/SG_img/colab_logo.png" /> Quantization Aware Training YoloNAS on Custom Dataset Notebook
         </a>
     </td>
 </tr>