Fashion Hash Net

Description

This responsitory contains the code of paper Learning Binary Code for Personalized Fashion Recommendation

Required Packages

• pytorch
• torchvision
• PIL
• numpy
• pandas
• tqdm: A Fast, Extensible Progress Bar for Python and CLI
• lmdb: A universal Python binding for the LMDB 'Lightning' Database.
• yaml: PyYAML is a full-featured YAML framework for the Python programming language.
• visdom: To start a visdom server run python -m visdom.server

I upgraded the version of PyTorch to 1.2.0 and the package dependency is solved automatically with conda.

The last 4 packages can be install via conda:

conda install python-lmdb pyyaml visdom tqdm -c conda-forge

How to Use the Code

The main script scripts/run.py currently supports the following functions:

ACTION_FUNS = {
    # train models
    "train": train,
    # runing the FITB task
    "fitb": fitb,
    # evaluate pairs accuracy
    "evaluate-accuracy": evalute_accuracy,
    # evaluate NDCG and AUC
    "evaluate-rank": evalute_rank,
    # compute the binary codes
    "extract-features": extract_features,
}

Configurations

There are three main modules in polyvore:

• polyvore.data: module for polyvore-dataset
• polyvore.model: module for fashion hash net
• polyvore.solver: module for training

For configurations, see polyvore.param, and we give some examples in cfg folder. The configuration file was written in yaml format.

Train

To train FHN-T3 with both visual and semantic features, run the following script:

scripts/run.py train --cfg ./cfg/train/FHN_VSE_T3_630.yaml

Evaluate

To evaluate the accuracy of positive-negative pairs:

scripts/run.py evaluate-accuracy --cfg ./cfg/evalute/FHN_VSE_T3_630.yaml

To evaluate the rank quality:

scripts/run.py evaluate-rank --cfg ./cfg/evaluate-rank/FHN_VSE_T3_630.yaml

To evaluate the FITB task:

scripts/run.py fitb --cfg ./cfg/fitb/FHN_VSE_T3_630.yaml

How to Use the Polyvore-$U$s

1. Download the data from OneDrive and put the polyvore folder under data;

2. Unzip the polyvore/images/291x291.tar.gz;

3. Use script/build_polyvore.py to convert images and save in data/polyvore/lmdb.

script/build_polyvore.py data/polyvore/images/291x291 data/polyvore/images/lmdb

The lmdb format can accelerate the load of images and set as default in configuration. If you don't want to use the lmdb format, change the setting to use_lmdb: false in yaml files.

See <data/README.md> for details

How to Cite

@inproceedings{Lu:2019tk,
author = {Lu, Zhi and Hu, Yang and Jiang, Yunchao and Chen, Yan and Zeng, Bing},
title = {{Learning Binary Code for Personalized Fashion Recommendation}},
booktitle = {CVPR},
year = {2019}
}

Contact

Email: [email protected]