MMFashion

Introduction

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

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

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}
}