# The iMaterialist Fashion Attribute Dataset

arXiv: https://arxiv.org/abs/1906.05750 · HTML: https://mlsanity.com/paper/1906.05750/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 30 of 37)

- [PACO: Parts and Attributes of Common Objects](https://mlsanity.com/paper/2301.01795/) (2023)
- [Learning to Predict Visual Attributes in the Wild](https://mlsanity.com/paper/2106.09707/) (2021)
- [COLA: A Benchmark for Compositional Text-to-image Retrieval](https://mlsanity.com/paper/2305.03689/) (2023)
- [INTERN: A New Learning Paradigm Towards General Vision](https://mlsanity.com/paper/2111.08687/) (2021)
- [Doubly Right Object Recognition: A Why Prompt for Visual Rationales](https://mlsanity.com/paper/2212.06202/) (2022)
- [CloSe: A 3D Clothing Segmentation Dataset and Model](https://mlsanity.com/paper/2401.12051/) (2024)
- [Meta-Learning for Multi-Label Few-Shot Classification](https://mlsanity.com/paper/2110.13494/) (2021)
- [Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark](https://mlsanity.com/paper/2207.11169/) (2022)
- [On Label Granularity and Object Localization](https://mlsanity.com/paper/2207.10225/) (2022)
- [Test-time Adaptation for Cross-modal Retrieval with Query Shift](https://mlsanity.com/paper/2410.15624/) (2024)
- [OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data](https://mlsanity.com/paper/2309.05551/) (2023)
- [FashionVQA: A Domain-Specific Visual Question Answering System](https://mlsanity.com/paper/2208.11253/) (2022)
- [Personalizing Pre-trained Models](https://mlsanity.com/paper/2106.01499/) (2021)
- [Representation Consolidation for Training Expert Students](https://mlsanity.com/paper/2107.08039/) (2021)
- [Training and challenging models for text-guided fashion image retrieval](https://mlsanity.com/paper/2204.11004/) (2022)
- [Cross-domain Multi-modal Few-shot Object Detection via Rich Text](https://mlsanity.com/paper/2403.16188/) (2024)
- [Same or Not? Enhancing Visual Perception in Vision-Language Models](https://mlsanity.com/paper/2512.23592/) (2025)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Labeled Data Selection for Category Discovery](https://mlsanity.com/paper/2406.04898/) (2024)
- [Seeing the Abstract: Translating the Abstract Language for Vision Language Models](https://mlsanity.com/paper/2505.03242/) (2025)
- [TextileNet: A Material Taxonomy-based Fashion Textile Dataset](https://mlsanity.com/paper/2301.06160/) (2023)
- [FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation](https://mlsanity.com/paper/2111.14145/) (2021)
- [VTON-IT: Virtual Try-On using Image Translation](https://mlsanity.com/paper/2310.04558/) (2023)
- [Adversarial Attacks on Hidden Tasks in Multi-Task Learning](https://mlsanity.com/paper/2405.15244/) (2024)
- [Automatic Generation of Fashion Images using Prompting in Generative Machine Learning Models](https://mlsanity.com/paper/2407.14944/) (2024)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [A machine learning pipeline for aiding school identification from child trafficking images](https://mlsanity.com/paper/2106.05215/) (2021)
- [ViM: Vision Middleware for Unified Downstream Transferring](https://mlsanity.com/paper/2303.06911/) (2023)
- [Automated Material Properties Extraction For Enhanced Beauty Product Discovery and Makeup Virtual Try-on](https://mlsanity.com/paper/2312.00766/) (2023)
- [AnyDesign: Versatile Area Fashion Editing via Mask-Free Diffusion](https://mlsanity.com/paper/2408.11553/) (2024)

## Abstract (arXiv)

Large-scale image databases such as ImageNet have significantly advanced image classification and other visual recognition tasks. However much of these datasets are constructed only for single-label and coarse object-level classification. For real-world applications, multiple labels and fine-grained categories are often needed, yet very few such datasets exist publicly, especially those of large-scale and high quality. In this work, we contribute to the community a new dataset called iMaterialist Fashion Attribute (iFashion-Attribute) to address this problem in the fashion domain. The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total. Each image is annotated by experts with multiple, high-quality fashion attributes. The result is the first known million-scale multi-label and fine-grained image dataset. We conduct extensive experiments and provide baseline results with modern deep Convolutional Neural Networks (CNNs). Additionally, we demonstrate models pre-trained on iFashion-Attribute achieve superior transfer learning performance on fashion related tasks compared with pre-training from ImageNet or other fashion datasets. Data is available at: https://github.com/visipedia/imat_fashion_comp