DeliNet

Deeplearning project for image2text conversion for images of food to textual recipes.

Data

• The Kaggle Food Ingredients and Recipes Dataset with Images
  • Download here

Prepare Kaggle Food Dataset

After downloading the Kaggle Food Ingredients and Recipes Dataset with Images we need it to be processed to fit our dataloader. To do this perform the following steps:

1. Save the archive.zip file that you have downloaded with the Kaggle Food Ingredients and Recipes Dataset with Images in the project folder data/raw
2. Run the command make prepare_kaggle_food_data

Then you're good to go. The raw data will be kept but renamed from archive.zip to KaggleFoodDataset.zip and the preprocessed data will be placed in data/processed/KaggleFoodDataset.

Project Structure

── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py

NB the folders data and models in the root folder are gitignored and should only be held locally to avoid pushing large files with git