This project implements a Fashion MNIST Classification system using the MNIST dataset. The model is trained to recognize Fashion objects like shirts,shoes,trousers etc. based on grayscale images of clothes. The project demonstrates the application of deep learning techniques for image recognition tasks.
- Introduction
- Features
- Installation
- Usage
- Project Structure
- Dependencies
- Examples
- Troubleshooting
- Contributors
- License
- Classification of Fashion Clothes
- Implementation in Jupyter Notebook for easy experimentation
- Deep learning model trained using standard datasets
- Visualizations of training performance and predictions
Clone the repository and install the required dependencies:
git clone https://github.com/129Ashish/Fashion_MNIST_Classification.git
cd Fashion_MNIST_Classification
pip install -r requirements.txtMake sure you have Python 3.7+ installed.
- Open the Jupyter Notebook:
jupyter notebook Fashion_MNIST_Classification.ipynb
- Run the cells step by step to:
- Load and preprocess the dataset
- Train the neural network model
- Evaluate accuracy on test data
- Visualize predictions
Fashion_MNIST_Classification/
│── Fashion_MNIST_Classification.ipynb # Main notebook
│── requirements.txt # Project dependencies
│── README.md # Documentation
Main dependencies (see requirements.txt for full list):
- numpy
- matplotlib
- Pytorch(deep learning framework)
- jupyter
Install all dependencies via:
pip install -r requirements.txt- Jupyter not found: Install with
pip install notebook. - Module errors: Re-run
pip install -r requirements.txt. - GPU not detected: Ensure TensorFlow GPU version and CUDA drivers are installed.