Awesome

MMFashion

Introduction

[Technical Report]

MMFashion is an open source visual fashion analysis toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Lab, CUHK.

Updates

[2019-11-01] MMFashion v0.1 is released.

[2020-02-14] MMFashion v0.2 is released, adding consumer-to-shop retrieval module.

[2020-04-27] MMFashion v0.3 is released, adding fashion segmentation and parsing module.

[2020-05-04] MMFashion v0.4 is released, adding fashion compatibility and recommendation module.

[2020-12-08] MMFashion v0.5 is released, adding virtual try-on module.

Features

• Flexible: modular design and easy to extend

• Friendly: off-the-shelf models for layman users

• Comprehensive: support a wide spectrum of fashion analysis tasks

  • Fashion Attribute Prediction
  • Fashion Recognition and Retrieval
  • Fashion Landmark Detection
  • Fashion Parsing and Segmentation
  • Fashion Compatibility and Recommendation
  • Fashion Virtual Try-On

Requirements

• Python 3.5+
• PyTorch 1.0.0+
• mmcv

Installation

git clone --recursive https://github.com/open-mmlab/mmfashion.git
cd mmfashion
python setup.py install

Another option: Docker Image

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.5, CUDA 10.1
docker build -t mmfashion docker/

Run it with

docker run --gpus all --shm-size=8g -it mmfashion

Get Started

Please refer to GETTING_STARTED.md for the basic usage of MMFashion.

Data Preparation

Please refer to DATA_PREPARATION.md for the dataset specifics of MMFashion.

Model Zoo

Please refer to MODEL_ZOO.md for a comprehensive set of pre-trained models in MMFashion.

Contributing

We appreciate all contributions to improve MMFashion. Please refer to CONTRIBUTING.md for the contributing guideline.

Related Tools

• fashion-detection
• fashion-landmarks
• fashion-cut

License

This project is released under the Apache 2.0 license.

Team

• Xin Liu (veralauee)
• Jiancheng Li (lijiancheng0614)
• Jiaqi Wang (myownskyW7)
• Ziwei Liu (liuziwei7)

Citation

@inproceedings{mmfashion,
  title={MMFashion: An Open-Source Toolbox for Visual Fashion Analysis},
  author={Liu, Xin and Li, Jiancheng and Wang, Jiaqi and Liu, Ziwei},
  booktitle={{ACM Multimedia 2021, Open Source Software Competition},
  year={2021}
}