https://github.com/rtlee9/food-gan

Novel food image generation using GANs
https://github.com/rtlee9/food-gan

Last synced: about 1 year ago
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Novel food image generation using GANs

• Host: GitHub
• URL: https://github.com/rtlee9/food-gan
• Owner: rtlee9
• License: mit
• Created: 2017-03-27T02:38:00.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2017-03-29T04:01:21.000Z (about 9 years ago)
• Last Synced: 2025-04-20T08:38:16.345Z (about 1 year ago)
• Language: Python
• Size: 1.74 MB
• Stars: 15
• Watchers: 5
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Food GAN

This repo implements a [Deep Convolutional GAN](https://arxiv.org/abs/1511.06434)(DCGAN) and a [Wasserstein GAN](https://arxiv.org/abs/1701.07875)(WGAN) to generate novel images of food.

## Data

I [scraped](https://github.com/rtlee9/recipe-box) ~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

![DCGAN](fake_DCGAN.png)

### WGAN

![WGAN](fake_WGAN.png)

## 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

![WGAN-loss-curve](WGAN_loss_history.png)

## 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