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 # MNIST CNN Model: Training, Quantization, and Evaluation
 
-
-This project demonstrates how to train a Convolutional Neural Network (CNN) on the MNIST dataset, quantize the trained model to INT8 using TensorFlow Lite, and evaluate the performance of the quantized model.
-
-## Project Overview
-The project consists of two Python scripts:
-
-- model.py: Handles the creation, training, and quantization of a CNN model.
-- evaluate.py: Evaluates the performance of the quantized INT8 model.
-
-### 1. model.py
-This script performs the following steps:
-
-- Model Definition: Defines a CNN architecture tailored for MNIST digit classification.
-- Data Preparation: Loads and preprocesses the MNIST dataset, normalizing the pixel values to the range [0, 1].
-- Model Training: Trains the CNN model on the MNIST training dataset for 5 epochs.
-- Model Evaluation: Evaluates the model on the test dataset and reports the accuracy.
-- Model Saving: Saves the trained model in full precision (FP32) format as cnn_fp32_model.
-- Model Quantization: Converts the FP32 model to an INT8 model using TensorFlow Lite, incorporating a representative dataset to optimize the quantization process while maintaining input and output in FP32.
-- Quantized Model Saving: Saves the quantized model as cnn_int8_model.tflite.
-
-### 2. evaluate.py
-This script is responsible for:
-
-- Model Loading: Loads the previously saved INT8 TensorFlow Lite model (cnn_int8_model.tflite).
-- Dataset Preparation: Loads and preprocesses the MNIST test dataset.
-- Model Evaluation: Runs inference on the test dataset using the quantized model and calculates the accuracy.
-- Results Reporting: Prints the accuracy of the INT8 model, allowing comparison with the original FP32 model.
-
-## Installation and Setup
-
-### 1. Environment Setup
-Before running the scripts, ensure your environment is properly configured.
-
-- Step 1: Clone the Repository
-```
-git clone https://github.com/OpenGenus/int8-training.git
-cd int8-training
-```
-
-- Step 2: Set Up the Environment (Install dependencies)
-
-```
-pip install NumPy
-pip install TensorFlow
-```
-
-### 2. Running the Scripts
-
-**To train and quantize the model, run:**
-
-```python3 model.py```
-
-This script will:
-
-- Train a CNN on the MNIST dataset.
-- Save the trained FP32 model as cnn_fp32_model.
-- Quantize the model to INT8 and save it as cnn_int8_model.tflite.
-
-**To evaluate the performance of the quantized model, run:**
-
-```python3 evaluate.py```
-
-This script will:
-
-- Load the cnn_int8_model.tflite file.
-- Run inference on the MNIST test dataset.
-- Print the accuracy of the quantized model.
-
-## Project Structure
-```
-├── model.py         # Script for training and quantizing the CNN model
-├── evaluate.py      # Script for evaluating the quantized model
-└──  README.md        # Project documentation (this file)
-```
-
-## Acknowledgements
-This project leverages the TensorFlow and Keras libraries for deep learning and model quantization. The MNIST dataset is a well-known dataset of handwritten digits, widely used for training and evaluating image processing systems.
-
-## Authors
-
-**Vidhi Srivastava**
-- [GitHub](https://github.com/Vidhi0229)
-- [LinkedIn](https://www.linkedin.com/in/vidhisrivastava01/)
-
-## Show Your Support ⭐️⭐️
-If you find this project helpful or interesting, please consider giving it a star!
-
-# MNIST CNN Model: Training, Quantization, and Evaluation
-
-
 This project demonstrates how to train a Convolutional Neural Network (CNN) on the MNIST dataset, quantize the trained model to INT8 using TensorFlow Lite, and evaluate the performance of the quantized model.
 
 ## Project Overview
 The project consists of two Python scripts:
 
-- model.py: Handles the creation, training, and quantization of a CNN model.
+- model.py: Handles a CNN model's creation, training, and quantization.
 - evaluate.py: Evaluates the performance of the quantized INT8 model.
 
 ### 1. model.py