Food GAN

This repo implements a Deep Convolutional GAN(DCGAN) and a Wasserstein GAN(WGAN) to generate novel images of food.

Data

I scraped ~125,000 recipes from various websites for use in this model. Each recipe consists of:

• A recipe title
• A list of ingredients
• Preparation instructions
• An image of the prepared recipe (missing for ~40% of recipes collected)

These GANs were fitted on the recipe images; they did not utilize the recipe's title, ingredients list, or instructions.

Sampled outputs

Below are a few randomly selected outputs from both the DCGAN and the WGAN:

DCGAN

WGAN

Training

Each model was trained for ~4 hours on an nVidia Tesla K80. The loss curve for the WGAN is depicted below:

WGAN loss curve

Usage (Python 3.6)

• Clone repo: git clone https://github.com/rtlee9/food-GAN.git; cd food-GAN
• Download images from my Google Cloud Bucket: wget -P imgs https://storage.googleapis.com/recipe-box/imgs.zip; unzip imgs/imgs.zip -d imgs (alternatively, see the recipe-box submodule to scrape fresh recipe data)
• Remove blacklisted images: ./src/rm_blacklist.sh
• Train and sample GAN: python src/pytorch_DCGAN.py --dataset folder --dataroot imgs --outf outputs or python src/pytorch_WGAN.py --dataset folder --dataroot imgs --outf outputs

Next steps

• Incorporate recipe text (title, ingredients, and instructions) to generate images conditioned on recipe context.
• Filter out placeholder images prior to training