MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
This toolbox addresses the need for a flexible and user-friendly toolkit in the fashion analysis domain, though it is incremental as it consolidates existing methods into a unified framework.
The authors introduced MMFashion, an open-source PyTorch toolbox for visual fashion analysis that supports tasks like attribute prediction, recognition, retrieval, landmark detection, parsing, segmentation, compatibility, and recommendation, aiming to provide a comprehensive platform for the research community.
We present MMFashion, a comprehensive, flexible and user-friendly open-source visual fashion analysis toolbox based on PyTorch. This toolbox supports a wide spectrum of fashion analysis tasks, including Fashion Attribute Prediction, Fashion Recognition and Retrieval, Fashion Landmark Detection, Fashion Parsing and Segmentation and Fashion Compatibility and Recommendation. It covers almost all the mainstream tasks in fashion analysis community. MMFashion has several appealing properties. Firstly, MMFashion follows the principle of modular design. The framework is decomposed into different components so that it is easily extensible for diverse customized modules. In addition, detailed documentations, demo scripts and off-the-shelf models are available, which ease the burden of layman users to leverage the recent advances in deep learning-based fashion analysis. Our proposed MMFashion is currently the most complete platform for visual fashion analysis in deep learning era, with more functionalities to be added. This toolbox and the benchmark could serve the flourishing research community by providing a flexible toolkit to deploy existing models and develop new ideas and approaches. We welcome all contributions to this still-growing efforts towards open science: https://github.com/open-mmlab/mmfashion.