Ratatouille

Multi-stage, hierarchical transformer-based encoder-decoder model to sequentially predict ingredients of a recipe, given an image and title. Introduced a co-attention module and batch contrastive triplet loss to maximize cross-modal fusion

Updated Dataloader running

1. Create a folder recipe1M_layers outside the codebase folder
2. Add the data (val/test) folder outside the codebase folder
3. Download and add cleaned_ingredients.json and cleaned_layers.json files inside recipe1M_layers folder
4. Run and use dataloader as before: the input tuple has been expanded to load the title, ingredients and instructions as text along with the other fields already present

Files can be found here: https://drive.google.com/drive/folders/14brtR12WlZ8fqvRttcv43wXkOfusSVUo?usp=sharing Link to Base Paper's GitHub page: https://github.com/torralba-lab/im2recipe-Pytorch Base paper link: http://pic2recipe.csail.mit.edu/