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Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

This is the PyTorch companion code for the paper:

Amaia Salvador, Erhan Gundogdu, Loris Bazzani, and Michael Donoser. Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning. CVPR 2021

If you find this code useful in your research, please consider citing using the following BibTeX entry:

@inproceedings{salvador2021revamping,
    title={Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning},
    author={Salvador, Amaia and Gundogdu, Erhan and Bazzani, Loris and Donoser, Michael},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2021}
}

Cloning

This repository uses git-lfs to store model checkpoint files. Make sure to install it before cloning by following the instructions here:

Once installed, model checkpoint files will be automatically downloaded when cloning the repository with:

git clone git@github.com:amzn/image-to-recipe-transformers.git

These files can optionally be ignored by using git lfs install --skip-smudge before cloning the repository, and can be downloaded at any time using git lfs pull.

Installation

• Create conda environment: conda env create -f environment.yml
• Activate it with conda activate im2recipetransformers

Data preparation

• Download & uncompress Recipe1M dataset. The contents of the directory DATASET_PATH should be the following:

layer1.json
layer2.json
train/
val/
test/

The directories train/, val/, and test/ must contain the image files for each split after uncompressing.

• Make splits and create vocabulary by running:

python preprocessing.py --root DATASET_PATH

This process will create auxiliary files under DATASET_PATH/traindata, which will be used for training.

Training

• Launch training with:

python train.py --model_name model --root DATASET_PATH --save_dir /path/to/saved/model/checkpoints

Tensorboard logging can be enabled with --tensorboard. Then, from the checkpoints directory run:

tensorboard --logdir "./" --port PORT

Run python train.py --help for the full list of available arguments.

Evaluation

• Extract features from the trained model for the test set samples of Recipe1M:

python test.py --model_name model --eval_split test --root DATASET_PATH --save_dir /path/to/saved/model/checkpoints

• Compute MedR and recall metrics for the extracted feature set:

python eval.py --embeddings_file /path/to/saved/model/checkpoints/model/feats_test.pkl --medr_N 10000

Pretrained models

• We provide pretrained model weights under the checkpoints directory. Make sure you run git lfs pull to download the model files.
• Extract the zip files. For each model, a folder named MODEL_NAME with two files, args.pkl, and model-best.ckpt is provided.
• Extract features for the test set samples of Recipe1M using one of the pretrained models by running:

python test.py --model_name MODEL_NAME --eval_split test --root DATASET_PATH --save_dir ../checkpoints

• A file with extracted features will be saved under ../checkpoints/MODEL_NAME.

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.