State of the art fashion recommendation system capturing user preference and capability of attribute preference interpretation
- The whole project was implemented on Google Colab, hence it is suggested to run it there itself!!
- Attribute_keras.ipynb - Attribute Representation Model Training
- GetAttribute.ipynb - Getting Visual representations from the Attribute Model
- GPBPR2.py - The Model File.
- train.py - Training file for our state of the art model.
- test.py - Testing our model
- Testing_Model.pynb - Notebook visualising our SOTA model and its predictions and outputs
- main.ipynb - To run train.py with the desired requirements
- "Attribute_keras.ipynb" and "GetAttribute.ipynb" extract visual features from an image .
- For the dataset please contact the original curators from this Paper
@misc{sagar2020paibpr,
title={PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability},
author={Dikshant Sagar and Jatin Garg and Prarthana Kansal and Sejal Bhalla and Rajiv Ratn Shah and Yi Yu},
year={2020},
eprint={2008.01780},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent