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Fashion MNIST Image Classification

This project is an implementation of image classification using the Fashion MNIST dataset. The Fashion MNIST dataset is a collection of 70,000 grayscale images in 10 different classes, with 7,000 images per class. The goal of this project is to build a machine learning model that can accurately classify these images into their respective classes.

The code for this project is written in Python and can be found in the 'Fashion MNIST Image Classification.ipynb' notebook in this repository. The notebook uses the Keras library to build a convolutional neural network (CNN) model and train it on the Fashion MNIST dataset.

Getting Started

To run this project, you will need to have Python 3 installed on your machine. You will also need to install the following libraries:

  • Keras
  • NumPy
  • Matplotlib
  • TensorFlow

You can install these libraries using pip. For example, to install Keras, you can run the following command:

pip install keras

Once you have installed the required libraries, you can clone this repository to your local machine using Git. To do this, run the following command:

git clone https://github.com/reeba212/Fashion_MNIST_Image_Classification.git

Running the Notebook

To run the notebook, navigate to the project directory in your terminal and run the following command:

jupyter notebook

This will open the Jupyter Notebook interface in your web browser. From here, you can open the Fashion MNIST Image Classification.ipynb notebook and run the cells to train and test the model.

Results

After training the model on the Fashion-MNIST dataset, we achieved an accuracy of 88.76% on the test set. This demonstrates that the CNN model is effective at classifying images in the Fashion-MNIST dataset.

Conclusion

This project shows how to build an image classification model using the Fashion MNIST dataset and the Keras library. With this knowledge, you can extend this project to work on other datasets and improve the accuracy of the model by fine-tuning the hyperparameters or using other techniques such as data augmentation.