This project focuses on building a machine learning model to classify handwritten digits using the MNIST dataset. The MNIST dataset is a well-known dataset in the field of image classification, containing 60,000 training images and 10,000 test images of handwritten digits from 0 to 9. The goal is to create a robust model capable of accurately predicting the digit in an unseen image.
The MNIST Digit Classification project is a deep learning-based approach to recognizing and classifying handwritten digits. Using various deep learning techniques, such as Convolutional Neural Networks (CNNs), the project demonstrates a step-by-step approach to achieving high accuracy in digit classification tasks.
- data/: Contains the raw and processed dataset files.
- notebooks/: Jupyter Notebooks that document the exploratory data analysis, preprocessing steps, model training, and evaluation processes.
- src/: Python scripts for data processing, model building, training, and evaluation.
- models/: Directory where trained models are saved.
- results/: Plots and other outputs generated from the training and evaluation processes.
- README.md: Overview and instructions for the project.
- requirements.txt: Dependencies required to run the project.
- setup.py: Installation script (if applicable).
- LICENSE: License information for the project.
To run this project, you'll need to have Python 3.x installed. Clone the repository and install the required dependencies:
git clone https://github.com/yourusername/mnist-digit-classification.git
cd mnist-digit-classification
pip install -r requirements.txt
## Usage
You can start by exploring the data and training the model using the Jupyter notebooks provided in the notebooks/ directory.
For instance, to run the data exploration notebook:
jupyter notebook notebooks/01_data_exploration.ipynb
To train the model, execute the following script:
python src/train.py
#Model Overview
The model used in this project is a Convolutional Neural Network (CNN) with the following layers:
Input layer: Takes 28x28 pixel images as input.
Convolutional layers: Extract features from the input images.
Pooling layers: Downsample the feature maps.
Fully connected layers: Map the features to the output space.
Output layer: A softmax layer with 10 units corresponding to the 10 digit classes (0-9)
# Results
The model achieved an accuracy of X% on the test dataset. Below are some of the key metrics:
Accuracy: X%
Precision: X%
Recall: X%
# Contributing
Contributions are welcome! Please fork the repository and submit a pull request if you have any improvements or new features to add.
# License
This project is licensed under the MIT License - see the LICENSE file for details.
### Explanation:
- **Sections**: The `README.md` file is structured with various sections that cover the essential details of your project.
- **Table of Contents**: It provides a quick navigation to different parts of the README.
- **Usage Instructions**: Step-by-step instructions for running your project.
- **Model Overview and Results**: These sections summarize the model architecture and key results from your project.
You can copy this text directly into your `README.md` file in your GitHub repository.
**a.** Would you like to generate the `requirements.txt` file based on typical libraries used for MNIST projects?
**b.** Need help with setting up a specific part of the project (e.g., model architecture in `model.py`)?