# SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images

arXiv: https://arxiv.org/abs/1905.11784 · HTML: https://mlsanity.com/paper/1905.11784/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 5 of 5)

- [FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion](https://mlsanity.com/paper/2206.11768/) (2022)
- [SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce](https://mlsanity.com/paper/2106.03532/) (2021)
- [Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce](https://mlsanity.com/paper/2208.06261/) (2022)
- [Human Body Shape Classification Based on a Single Image](https://mlsanity.com/paper/2305.18480/) (2023)
- [RulerNet: Learning Perspective-Invariant Ruler Representations for Robust Image Scale Estimation](https://mlsanity.com/paper/2507.07077/) (2025)

## Abstract (arXiv)

Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms every day, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly-supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.