ARTEST

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

What things you need to install the software and how to install them


numpy
flask
cpickle
tensorflow
scipy
tqdm

Installing

A step by step series of examples that tell you how to get a development env running

If the required dependencies (prerequisites) are already installed, please skip this part. To install these prerequisites, Open the respective command line interface (powershell, terminal) and paste the following:

pip install numpy
pip install scipy
pip install tqdm
pip install tensorflow
pip install flask
py -m pip install --user virtualenv

To create a virtual environment, open the directory of the project through the command line and enter ther following command, ''' python -m virtualenv env ''' Now, there will be an (env) on the side of the terminal line.

Datasets :

Download the dataset (36GB) and extract it in the misc/fullimages https://drive.google.com/file/d/1yHqS2zXgCiI9LO4gN-X5W18QYXC5bbQS/view

Description :

Artest is a program used to generate a completely new painting by using Generative adversarial model.

Usage :

To use our tool, connect to NUS-Guest wifi network and type in the following IP Address: http://172.17.212.68:5000/ Please follow the steps listed there to try out our tool. Do take some time to check out our Facebook, Instagram and G+ pages and feel free to contact us via email as given on the website.

Prize Categories :

Categories we would like to enrol for :

  1. Top 8
  2. Most Beautiful Hack
  3. Most Annoying Hack
  4. Most Entertaining Hack
  5. Most Awesomely Useless Hack
  6. Coreteam's Best Roll

Team member Contribution :

  1. Siddesh - Backend Developer ( ID number 610 )
  2. Joe - Server Setup ( ID number - 589 )
  3. Vijai - Social Media setup and Server setup ( ID number 595 )
  4. Rohan - Supervising and assisting Server Setup ( ID number 563 )

Finally,Pull requests/changes/stars would be really helpful.


Inspired by: Generative Adversarial Networks Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio https://arxiv.org/abs/1406.2661v1

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