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Fashion_Image_Generation_Project

Fashion Image Generation Project by Generative Adversarial Networks (GANs) on Fashion MNIST

A Group Project of Computer Vision (COMP4423) subject at The Hong Kong Polytechnic University, made by JIANG Yiyang, YE Haowen, and ZHANG Wengyu, JIANG Yiyang and YE Haowen

• Web Application on Localhost:

• Generated Fashion Images:

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Group Members

• JIANG Yiyang
• YE Haowen
• ZHANG Wengyu

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Dataset

Fashion MNIST Dataset

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Project Models

1. Basic GAN

originalGAN/GAN.ipynb

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2. Conditional GAN

cGAN/cGAN.ipynb

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3. Conditional Deep Convolutional GAN

cDCGAN/cDCGAN.ipynb

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4. Auxiliary Classifier GAN

ACGAN.ipynb

(Figure: https://github.com/clvrai/ACGAN-PyTorch)

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Comparison

Model	Generated Images
GAN	Fashion Images
cGAN	Fashion Images
cDCGAN	Fashion Images
ACGAN	Fashion Images

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User Manual

Preparation

1. Python version: 3.10.10
2. Make sure following package are install for python3:
   • OpenCV, Flask, numpy, pandas, sklearn, torch, torchvision, torchaudio, matplotlib, seaborn, PIL
3. Download the Fashion MNIST Dataset and rename fashion-mnist_test.csv, and fashion-mnist_train.csv as train.csv, and test.csv respectively, then put two csv files in the newly-created fashion-mnist folder under this folder.

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Run and use the Fashion Clothing Image Generator

• Make sure you have installed all the required packages mentioned in the Preparation section above;
• Change directory to the this folder;
• Run the Fashion_Image_Generator.py file by python3 Fashion_Image_Generator.py
• Check your terminal says Running on http://127.0.0.1:5000/ (Press CTRL+C to quit);
• Use your browser to visit the web page on your localhost: http://127.0.0.1:5000/;
• follow the instruction on the web page (step1, step2);
• Press CTRL+C in terminal to quit.

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View dataset, train and validate models

Note: We have tried 4 different models, original provided GAN, Conditional GAN, Conditional Deep Convolutional GAN (cDCGAN), Auxiliary Classifier GAN (ACGAN). The ACGAN model is our final model, which is used in the project.

1. Open the ACGAN.ipynb Jupyter Notebook on local machine;
2. In the first code cell, please set proper device according to your machine.
3. Run all cells in the ACGAN.ipynb file;
4. You may also try other models which are in the originalGAN, cGAN, cDCGAN folders.

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Disclaimer of Liability

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