sony · ofirgo · May 9, 2024 · May 9, 2024
diff --git a/README.md b/README.md
@@ -41,9 +41,9 @@ For installing the nightly version or installing from source, refer to the [inst
 Explore the Model Compression Toolkit (MCT) through our tutorials, 
 covering compression techniques for Keras and PyTorch models. Access interactive [notebooks](tutorials/README.md) 
 for hands-on learning. For example:
-* [Keras MobileNetV2 post training quantization](tutorials/notebooks/keras/ptq/example_keras_imagenet.ipynb)
-* [Post training quantization with PyTorch](tutorials/notebooks/pytorch/ptq/example_pytorch_quantization_mnist.ipynb)
-* [Data Generation for ResNet18 with PyTorch](tutorials/notebooks/pytorch/data_generation/example_pytorch_data_generation.ipynb).
+* [Keras MobileNetV2 post training quantization](tutorials/notebooks/imx500_notebooks/keras/example_keras_mobilenetv2_for_imx500.ipynb)
+* [Post training quantization with PyTorch](tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_ptq_mnist.ipynb)
+* [Data Generation for ResNet18 with PyTorch](tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_data_generation.ipynb).
 
 
 ### Supported Versions
@@ -146,8 +146,6 @@ In the following table we present the ImageNet validation results for these mode
 | ResNet-18 [3]             | 69.86           | 69.63           |69.53|
 | SqueezeNet 1.1 [3]        | 58.128          | 57.678          ||
 
-For more results, please refer to [quick start](https://github.com/sony/model_optimization/tree/main/tutorials/quick_start).
-
 
 #### Pruning Results
 

diff --git a/docsrc/source/index.rst b/docsrc/source/index.rst
@@ -79,7 +79,7 @@ Quickstart
 ====================================
 Take a look of how you can start using MCT in just a few minutes!
 
-Visit our `notebooks <https://github.com/sony/model_optimization/tree/main/tutorials/notebooks>`_ and `MCT quick start. <https://github.com/sony/model_optimization/tree/main/tutorials/quick_start>`_
+Visit our `notebooks <https://github.com/sony/model_optimization/tree/main/tutorials/notebooks>`_
 
 
 API Documentation

diff --git a/model_compression_toolkit/data_generation/README.md b/model_compression_toolkit/data_generation/README.md
@@ -19,7 +19,7 @@ pip install model-compression-toolkit
 ## Usage
 ### PyTorch
 Explore a Jupyter Notebook example showcasing data generation with ResNet18, including visualizations, and a practical example of Post Training Quantization:
-* [Data Generation for Resnet18 with PyTorch](../../tutorials/notebooks/pytorch/data_generation/example_pytorch_data_generation.ipynb).
+* [Data Generation for Resnet18 with PyTorch](../../tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_data_generation.ipynb).
 
 Below, you will find a straightforward usage example:
 ```python

diff --git a/model_compression_toolkit/exporter/README.md b/model_compression_toolkit/exporter/README.md
@@ -14,4 +14,4 @@ You can export your quantized model in the following quantization formats:
 
 ## Usage Examples
 
-Try our notebooks to export [Keras](../../tutorials/notebooks/keras/export/example_keras_export.ipynb) or [Pytorch](../../tutorials/notebooks/pytorch/export/example_pytorch_export.ipynb) models using different formats.
+Try our notebooks to export [Keras](../../tutorials/notebooks/mct_features_notebooks/keras/example_keras_export.ipynb) or [Pytorch](../../tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_export.ipynb) models using different formats.
diff --git a/tutorials/README.md b/tutorials/README.md
@@ -6,21 +6,21 @@ Access interactive Jupyter notebooks for hands-on learning.
 
 ## Getting started
 Learn how to quickly quantize pre-trained models using MCT's post-training quantization technique for both Keras and PyTorch models.
-- [Post training quantization with Keras](notebooks/keras/ptq/example_keras_imagenet.ipynb)
-- [Post training quantization with PyTorch](notebooks/pytorch/ptq/example_pytorch_quantization_mnist.ipynb)
+- [Post training quantization with Keras](notebooks/imx500_notebooks/keras/example_keras_mobilenetv2_for_imx500.ipynb)
+- [Post training quantization with PyTorch](notebooks/mct_features_notebooks/pytorch/example_pytorch_ptq_mnist.ipynb)
 
 ## MCT Features
 This set of tutorials covers all the quantization tools provided by MCT. 
 The notebooks in this section demonstrate how to configure and run simple and advanced post-training quantization methods.
 This includes fine-tuning PTQ (Post-Training Quantization) configurations, exporting models,
 and exploring advanced compression techniques. 
 These techniques are essential for further optimizing models and achieving superior performance in deployment scenarios.
-- [MCT Features notebooks](notebooks/MCT_notebooks.md)
+- [MCT Features notebooks](notebooks/mct_features_notebooks/README.md)
 
 ## Quantization for Sony-IMX500 deployment
 
 This section provides several guides on quantizing pre-trained models to meet specific constraints for deployment on the
 [Sony-IMX500](https://developer.sony.com/imx500/) processing platform. 
 We will cover various tasks and demonstrate the necessary steps to achieve efficient quantization for optimal
 deployment performance.
-- [MCT IMX500 notebooks](notebooks/IMX500_notebooks.md)
+- [MCT IMX500 notebooks](notebooks/imx500_notebooks/README.md)
diff --git a/tutorials/notebooks/IMX500_notebooks.md → ...ials/notebooks/imx500_notebooks/README.md b/tutorials/notebooks/IMX500_notebooks.md → ...ials/notebooks/imx500_notebooks/README.md
@@ -14,19 +14,19 @@ deployment performance.
     <!-- Classification Models (ImageNet) -->
     <tr>
         <td rowspan="2">Classification</td>
-        <td> <a href="keras/ptq/example_keras_imagenet.ipynb">MobilenetV2</a></td>
+        <td> <a href="keras/example_keras_mobilenetv2_for_imx500.ipynb">MobilenetV2</a></td>
         <td>Keras</td>
         <td>ImageNet</td>
     </tr>
     <tr>
-        <td> <a href="pytorch/ptq/pytorch_mobilevit_xs_for_imx500.ipynb">MobileVit</a></td>
+        <td> <a href="pytorch/pytorch_mobilevit_xs_for_imx500.ipynb">MobileVit</a></td>
         <td>PyTorch</td>
         <td>ImageNet</td>
     </tr>
     <!-- Object Detection Models (COCO) -->
     <tr>
         <td rowspan="4">Object Detection</td>
-        <td> <a href="keras/ptq/keras_yolov8n_for_imx500.ipynb">YOLOv8n</a></td>
+        <td> <a href="keras/keras_yolov8n_for_imx500.ipynb">YOLOv8n</a></td>
         <td>Keras</td>
         <td>COCO</td>
     </tr>
@@ -36,18 +36,18 @@ deployment performance.
         <td>COCO</td>
     </tr>
     <tr>
-        <td> <a href="keras/ptq/example_keras_nanodet_plus.ipynb">NanoDet-Plus-m-416</a></td>
+        <td> <a href="keras/example_keras_nanodet_plus_for_imx500.ipynb">NanoDet-Plus-m-416</a></td>
         <td>Keras</td>
         <td>COCO</td>
     </tr>
     <tr>
-        <td> <a href="keras/ptq/example_keras_effdet_lite0.ipynb">EfficientDet-lite0</a></td>
+        <td> <a href="keras/example_keras_effdet_lite0_for_imx500.ipynb">EfficientDet-lite0</a></td>
         <td>Keras</td>
         <td>COCO</td>
     </tr>
     <tr>
         <td>Semantic Segmentation</td>
-        <td> <a href="keras/ptq/keras_deeplabv3plus_for_imx500.ipynb">Deeplabv3plus</a></td>
+        <td> <a href="keras/keras_deeplabv3plus_for_imx500.ipynb">Deeplabv3plus</a></td>
         <td>Keras</td>
         <td>PASCAL VOC</td>
     </tr>

diff --git a/...als/notebooks/pytorch/pruning/__init__.py → ...ls/notebooks/imx500_notebooks/__init__.py b/...als/notebooks/pytorch/pruning/__init__.py → ...ls/notebooks/imx500_notebooks/__init__.py
diff --git a/tutorials/notebooks/imx500_notebooks/keras/__init__.py b/tutorials/notebooks/imx500_notebooks/keras/__init__.py
diff --git a/...eras/ptq/example_keras_effdet_lite0.ipynb → ...ample_keras_effdet_lite0_for_imx500.ipynb b/...eras/ptq/example_keras_effdet_lite0.ipynb → ...ample_keras_effdet_lite0_for_imx500.ipynb
diff --git a/...ks/keras/ptq/example_keras_imagenet.ipynb → ...xample_keras_mobilenetv2_for_imx500.ipynb b/...ks/keras/ptq/example_keras_imagenet.ipynb → ...xample_keras_mobilenetv2_for_imx500.ipynb
diff --git a/...eras/ptq/example_keras_nanodet_plus.ipynb → ...ample_keras_nanodet_plus_for_imx500.ipynb b/...eras/ptq/example_keras_nanodet_plus.ipynb → ...ample_keras_nanodet_plus_for_imx500.ipynb
diff --git a/.../ptq/keras_deeplabv3plus_for_imx500.ipynb → ...eras/keras_deeplabv3plus_for_imx500.ipynb b/.../ptq/keras_deeplabv3plus_for_imx500.ipynb → ...eras/keras_deeplabv3plus_for_imx500.ipynb
diff --git a/.../keras/ptq/keras_yolov8n_for_imx500.ipynb → ...ooks/keras/keras_yolov8n_for_imx500.ipynb b/.../keras/ptq/keras_yolov8n_for_imx500.ipynb → ...ooks/keras/keras_yolov8n_for_imx500.ipynb
diff --git a/tutorials/notebooks/imx500_notebooks/pytorch/__init__.py b/tutorials/notebooks/imx500_notebooks/pytorch/__init__.py
diff --git a/...ptq/pytorch_mobilevit_xs_for_imx500.ipynb → ...rch/pytorch_mobilevit_xs_for_imx500.ipynb b/...ptq/pytorch_mobilevit_xs_for_imx500.ipynb → ...rch/pytorch_mobilevit_xs_for_imx500.ipynb
diff --git a/...orch/ptq/pytorch_yolov8n_for_imx500.ipynb → .../pytorch/pytorch_yolov8n_for_imx500.ipynb b/...orch/ptq/pytorch_yolov8n_for_imx500.ipynb → .../pytorch/pytorch_yolov8n_for_imx500.ipynb
diff --git a/tutorials/notebooks/keras/__init__.py b/tutorials/notebooks/keras/__init__.py
diff --git a/tutorials/notebooks/keras/data_generation/__init__.py b/tutorials/notebooks/keras/data_generation/__init__.py
diff --git a/tutorials/notebooks/keras/debug_tools/__init__.py b/tutorials/notebooks/keras/debug_tools/__init__.py
diff --git a/tutorials/notebooks/keras/export/__init__.py b/tutorials/notebooks/keras/export/__init__.py
diff --git a/tutorials/notebooks/keras/gptq/__init__.py b/tutorials/notebooks/keras/gptq/__init__.py
diff --git a/tutorials/notebooks/keras/pruning/__init__.py b/tutorials/notebooks/keras/pruning/__init__.py
diff --git a/tutorials/notebooks/keras/ptq/__init__.py b/tutorials/notebooks/keras/ptq/__init__.py
diff --git a/tutorials/notebooks/keras/qat/__init__.py b/tutorials/notebooks/keras/qat/__init__.py
Original file line number	Diff line number	Diff line change
Expand Up		@@ -14,4 +14,4 @@ You can export your quantized model in the following quantization formats:

		## Usage Examples

		Try our notebooks to export [Keras](../../tutorials/notebooks/keras/export/example_keras_export.ipynb) or [Pytorch](../../tutorials/notebooks/pytorch/export/example_pytorch_export.ipynb) models using different formats.
		Try our notebooks to export [Keras](../../tutorials/notebooks/mct_features_notebooks/keras/example_keras_export.ipynb) or [Pytorch](../../tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_export.ipynb) models using different formats.