Remove uneeded *py tutorials and edit the tutorials readm

tutorials/README.md

@@ -0,0 +1,26 @@
+# MCT Tutorials
+Explore the Model Compression Toolkit (MCT) through our tutorials, 
+covering compression techniques for Keras and PyTorch models. 
+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.
+- [Keras MobileNetV2 post training quantization](notebooks/keras/ptq/example_keras_imagenet.ipynb)
+- [Pytorch MobileNetV2 post training quantization](notebooks/pytorch/ptq/example_pytorch_quantization_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 notebooks](notebooks/MCT_notebooks.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.
+- [IMX500 notebooks](notebooks/IMX500_notebooks.md)

tutorials/notebooks/IMX500_notebooks.md

@@ -0,0 +1,12 @@
+# Sony-IMX500 Notebooks
+
+Here we provide examples on quantizing pre-trained models for deployment on Sony-IMX500 processing platform.
+We will cover various tasks and demonstrate the necessary steps to achieve efficient quantization for optimal
+deployment performance.
+
+  | Task                                                            | Model          | Source Repository                                                   | Notebook                                                                                                |
+  |-----------------------------------------------------------------|----------------|---------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|
+  | Classification                                                  | MobileNetV2    | [Keras Applications](https://keras.io/api/applications/)            | [Keras notebook](model_optimization/tutorials/notebooks/keras/ptq/example_keras_imagenet.ipynb)         |
+  | Object Detection                                                | YOLOv8n        | [Ultralytics](https://github.com/ultralytics/ultralytics)           | [Keras notebook](model_optimization/tutorials/notebooks/keras/ptq/keras_yolov8n_for_imx500.ipynb)       | 
+  | Semantic Segmentation                                           | DeepLabV3-Plus | [bonlime's repo](https://github.com/bonlime/keras-deeplab-v3-plus)  | [Keras notebook](model_optimization/tutorials/notebooks/keras/ptq/keras_deeplabv3plus_for_imx500.ipynb) |
+

tutorials/notebooks/README.md → tutorials/notebooks/MCT_notebooks.md

@@ -1,30 +1,11 @@
-# Tutorials
-
-## Table of Contents
-- [Introduction](#introduction)
-- [Keras Tutorials](#keras-tutorials)
-  - [Post-Training Quantization](#keras-ptq)
-  - [Gradient-Based Post-Training Quantization](#keras-gptq)
-  - [Quantization-Aware Training](#keras-qat)
-  - [Structured Pruning](#keras-pruning)
-  - [Export Quantized Models](#keras-export)
-  - [Debug Tools](#keras-debug)
-- [Pytorch Tutorials](#pytorch-tutorials)
-  - [Quick-Start with Torchvision](#pytorch-quickstart-torchvision)
-  - [Post-Training Quantization](#pytorch-ptq)
-  - [Quantization-Aware Training](#pytorch-qat)
-  - [Structured Pruning](#pytorch-pruning)
-  - [Data Generation](#pytorch-data-generation)
-  - [Export Quantized Models](#pytorch-export)
-
-## Introduction
-Dive into the Model-Compression-Toolkit (MCT) with our collection of tutorials, covering a wide 
-range of compression techniques for Keras and Pytorch models. We provide
-both Python scripts and interactive Jupyter notebooks for an
-engaging and hands-on experience.
-
-
-## Keras Tutorials
+# MCT Features
+This tutorial set introduces the various quantization tools offered by MCT. 
+The notebooks included here illustrate the setup and usage of both basic and advanced post-training quantization methods. 
+You'll learn how to refine PTQ (Post-Training Quantization) settings, export models, and explore advanced compression 
+techniques such as GPTQ (Gradient-Based Post-Training Quantization), Mixed precision quantization and more.
+These techniques are essential for further optimizing models and achieving superior performance in deployment scenarios.
+
+### Keras Tutorials
 
 <details id="keras-ptq">
   <summary>Post-Training Quantization (PTQ)</summary>
@@ -85,7 +66,7 @@ engaging and hands-on experience.
 
 </details>
 
-## Pytorch Tutorials
+### Pytorch Tutorials
 
 
 <details id="pytorch-quickstart-torchvision">
@@ -117,6 +98,7 @@ engaging and hands-on experience.
   | Tutorial                                                                          | Included Features      |
   |-----------------------------------------------------------------------------------|--------------|
   | [QAT on MNIST](pytorch/qat/example_pytorch_qat.py) | &#x2705; QAT |
+</details>
 
 </details>
 
@@ -148,4 +130,3 @@ engaging and hands-on experience.
   | [Exporter Usage](pytorch/export/example_pytorch_export.ipynb) | &#x2705; Export |
 
 </details>
-