Outfit Completion with Siamese Networks

This repository contains the code used for the experiments in "Unimodal vs. Multimodal Siamese Networks for Outfit Completion" submitted to the SIGIR 2022 Workshop on eCommerce outfit completion challenge.

Citation

If you use this code to produce results for your scientific publication, or if you share a copy or fork, please refer to our paper:

@article{hendriksen-overes-2022-unimodal,
  title={Unimodal vs. Multimodal Siamese Networks for Outfit Completion},
  author={Hendriksen, Mariya and Overes, Viggo},
  journal={arXiv preprint arXiv:2207.10355},
  year={2022}
}

Task Description

Outfit completion is a task when, given an item, the model is to recommend products that complete the outfit. In this project, we consider the task of outfit completion in the context of the Farfetch Data Challenge.

Since the dataset used is multimodal, the task can be approached using multimodal machine learning. Here we look at using text and image data to train the model and its performance with unimodal and multimodal product representations. To do this we compose a Siamese neural network with the CLIP model in a zero-shot setting to train an encoder for the products. The CLIP model will use its langauge and vision portions based on the input modality. This encoder will learn a mapping where products that fit together map to a similar point and products that don't to dissimilar points, since we are using contrastive loss with our Siamese network. You can see the configuration for the multimodal Siamese network in the figure below.

The result is an embedding that can be used for outfit completion. To evaluate the model, we use the task called fill-in-the-blank (FITB), where the model has to recommend the missing item from an outfit.

All Python files are implemented with argparse. By using -h and --help a brief description will be displayed for each argument. The Python versions used during development are 3.8 and 3.9.

Requirements

• PyTorch
• CLIP (GitHub Repo)
• Pandas
• PyArrow
• NumPy
• Pillow
• scikit-learn

For versions and more packages used, see requirements.txt. Install all requirements by running pip install -r requirements.txt

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Preparing Data

The initial files provided by Farfetch as part of their Fashion Outfits Challenge are products.parquet, manual_outfits.parquet and the directory images containing product images for all products in the dataset. These should all be present in the dataset directory.

The following files are used for preprocessing this data to use with the model:

1. Use data_farfetch.py to convert the text product data into sentences used for training.
   • Produces files: products_text_image.parquet
2. Use process_data.py to convert the manual outfits data into FITB queries that will be used to evaluate the model.
   • Produces files: outfits.parquet
3. Use process_pairs.py to process the data into pairs and preprocess part of the data for faster execution.
   • Produces files: pairs.npy, processed_text.npy, processed_image_part.npy

To simplify the preprocessing, you can use the following command: ./preprocess.sh dataset-directory. This will run all the processing needed with the files listed above.

Usage

The following files are used to train, evaluate and make predictions with the model:

• Use clip_siamese.py to train the Siamese networks with different modalities.
  • Files needed: pairs.npy, processed_text.npy, processed_image_part.npy, products_text_image.parquet
• Use clip_embedding.py to use either CLIP in a zero-shot setting or a trained encoder to answer FITB queries.
  • Files needed: products_text_image.parquet, processed_text.npy, processed_image_part.npy, outfits.parquet (when file with FITB queries not provided)

Paper

Link to arxiv entry.