https://github.com/alihassanml/mnist-dataset-prediction
This repository contains the code for training and predicting digits from the MNIST dataset using a Convolutional Neural Network (CNN). The MNIST dataset is a well-known dataset in the machine learning community, consisting of 28x28 grayscale images of handwritten digits (0-9).
https://github.com/alihassanml/mnist-dataset-prediction
Last synced: 6 months ago
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This repository contains the code for training and predicting digits from the MNIST dataset using a Convolutional Neural Network (CNN). The MNIST dataset is a well-known dataset in the machine learning community, consisting of 28x28 grayscale images of handwritten digits (0-9).
- Host: GitHub
- URL: https://github.com/alihassanml/mnist-dataset-prediction
- Owner: alihassanml
- License: mit
- Created: 2024-08-12T20:16:00.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-12T20:50:32.000Z (about 1 year ago)
- Last Synced: 2025-02-16T01:12:50.025Z (8 months ago)
- Language: Jupyter Notebook
- Size: 4.04 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MNIST Dataset Prediction
This repository contains the code for training and predicting digits from the MNIST dataset using a Convolutional Neural Network (CNN). The MNIST dataset is a well-known dataset in the machine learning community, consisting of 28x28 grayscale images of handwritten digits (0-9).
## Table of Contents
- [Introduction](#introduction)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Predicting Custom Images](#predicting-custom-images)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
## Introduction
The goal of this project is to build a deep learning model that can accurately classify handwritten digits from the MNIST dataset. The model is built using TensorFlow/Keras and is capable of predicting digits from custom images as well.
## Model Architecture
The model is a simple CNN that consists of the following layers:
- **Conv2D**: Convolutional layers to extract features from the input images.
- **MaxPooling2D**: Pooling layers to reduce the spatial dimensions of the feature maps.
- **Flatten**: Converts the 2D matrix data to a 1D vector.
- **Dense**: Fully connected layers to perform the classification.
### Summary of the Model
```
Conv2D -> MaxPooling2D -> Conv2D -> MaxPooling2D -> Flatten -> Dense -> Dense
```
## Dataset
The MNIST dataset contains 70,000 images of handwritten digits, split into:
- **Training Set**: 60,000 images
- **Test Set**: 10,000 images
Each image is 28x28 pixels and is labeled with a digit from 0 to 9.
## Installation
To get started, clone this repository and install the required dependencies.
```bash
git clone https://github.com/alihassanml/Mnist-Dataset-Prediction.git
cd Mnist-Dataset-Prediction
pip install -r requirements.txt
```
## Usage
### Training the Model
To train the model on the MNIST dataset, run the following command:
```bash
python train.py
```
### Loading a Pre-trained Model
If you already have a trained model, you can load it and use it for predictions:
```python
import tensorflow as tf
model = tf.keras.models.load_model('mnist_model.h5')
```
### Predicting Custom Images
To predict a digit from a custom image, follow these steps:
1. Place your custom image in the project directory.
2. Run the following script:
```python
from predict_custom_image import predict_digit
image_path = 'custom_digit.png'
predicted_digit = predict_digit(image_path)
print(f'The predicted digit is: {predicted_digit}')
```
Make sure the custom image is a 28x28 grayscale image. If not, the script will resize and preprocess it accordingly.
## Results
The model achieves over 99% accuracy on the MNIST test set. Below are some sample predictions:
## Contributing
Contributions are welcome! If you have any improvements, suggestions, or new features to add, feel free to open an issue or create a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.