Fashion-Classification

A ML and DS project on fashion products classification based on MNIST fashion dataset.

Table of Contents

• Fashion-Classification
  • Table of Contents
  • About the Project
  • Dataset
  • Project Structure
  • Dependencies
  • Model
  • How to run (Windows)

About the Project

The goal of this project is to classify fashion products based on the MNIST fashion dataset. The dataset contains 10 classes of fashion products.

Dataset

The dataset used for this project is the Fashion MNIST

Project Structure

The models folder contains the trained model. The notebooks folder contains the exploratory data analysis and model training notebooks. The evaluate_model.py script is used to evaluate the model on the test set and save the output in the output.txt file.

Fashion-Classification
│
├── dataset
│   └── fashion-mnist_test.csv
│
├── models
│   └── model_v2.pkl
│
├── notebooks
│   ├── model_training.ipynb
│   └── exploratory_data_analysis.ipynb
│
├── evaluate_model.py
├── model_summary.txt
├── output.txt
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt

Dependencies

• fastai
• scikit-learn

Model

The model used for this project is a ResNet34 model with custom head layer which is trained and dine-tuned on the Fashion MNIST dataset. The model was trained for 5 epochs with a batch size of 32. The model achieved an accuracy of 89.9% on the test set.

How to run (Windows)

1. Clone the repository and navigate to the project directory.

git clone https://github.com/abir0/Fashion-Classification.git
cd Fashion-Classification

2. Create a virtual environment and activate it.

python -m venv venv
venv\Scripts\activate

3. Install the dependencies

pip install -r requirements.txt

1. Run the evaluate_model.py script

python evaluate_model.py

5. The output will be saved in the output.txt file.