Fashionpedia: The Visual Dictionary of Fashion Design

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What sets FASHIONPEDIA apart from the others is its visual oriented layout. We understand designers communicate best in visual and images. That’s why we’ve converted all complex textile information into info-graphics and beautiful charts which make the information so easy to read, understand and remember. 3. Compact & Sleek author={Jia, Menglin and Shi, Mengyun and Sirotenko, Mikhail and Cui, Yin and Cardie, Claire and Hariharan, Bharath and Adam, Hartwig and Belongie, Serge} Fashionpedia is a new dataset which consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with 48k everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. Fashionpedia covers History and Styles, Apparel, Detail, Accessories, Textile, Manufacturing, Body & Beauty, Measurement & Care.

FASHIONPEDIA is a visual fashion dictionary covering all the technical terms from style to material to production with illustrations and infographics. It encompasses rich, extensive information and yet is so easy to read. Whether you’re an industry insider or a fashion connoisseur, FASHIONPEDIA is all you’ll ever need to navigate the fashion scene.Students will benefit greatly from the content of FASHIONPEDIA. What they get is a fashion library in their hand covering all the common items and details as well as material and manufacturing knowledge.

With the introduction of the dataset, we explore the new task of instance segmentation with attribute localization. The proposed task requires both localizing an object and describing its properties, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes).Designed to be as visually driven as the people who use it, Fashionpedia contains thousands of fashion items, converting unapproachable technical terms on style, material and production into beautiful charts and infographics. Fashionpedia is a new dataset which consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology.

An extensive textile dictionary that covers all essential fabric knowledge from different types of textile, fabric materials to finishing options. There are all together 8 chapters in FASHIONPEDIA including fashion history, apparel, details library, accessories, textile, manufacturing, body & beauty, measurement & care. We are hosting Kaggle challenge (under name iMaterialist-Fashion) using Fashionpedia dataset under FGVC (Fine-grained Visual Categorization workshop) at CVPR. Un libro estilo mini-diccionario visual, para conocer la terminología básica de las prendas de moda. We present a new clothing dataset with the goal of introducing a novel fine-grained segmentation task by joining forces between the fashion and computer vision communities. The proposed task unifies both categorization and segmentation of rich and complete apparel attributes, an important step toward real-world applications.Fashion design is a combination of three important factors: imagination, fabrication, and execution.

Fashionary is the survival kit for fashion week. It collaborated with fashion brands like Alexander McQueen, Kurt Geiger, Colette, Yazbukey, Henrik Viskov etc… ABOUT US Founded in 20018, Fashionary publishes the 1 st sketchbook tail-made for fashion designers. The sketchbook design is a life-saving product for professionals and working at all levels in the fashion industry. python3 -m venv env # Create a virtual environment source env/bin/activate # Activate virtual environment # step 1: install COCO API: # Note: COCO API requires numpy to install. Ensure that you have numpy installed. # e.g. pip install numpy Visual analysis of clothing is a topic that has received increasing attention in recent years. Being able to recognize apparel products and associated attributes from pictures could enhance shopping experience for consumers, and increase work efficiency for fashion professionals. We focus on presenting information using the most practical mindset possible, making knowledge easy to digest and apply.Textilepedia covers Fibers, Yarn, Weave, Knits, Lace & Netting, Non-woven & Felting, Hides, Finishings, Patterns & Colors. Please report metadata errors at the source library. If there are multiple source libraries, know that we pull metadata from top to bottom, so the first one might be sufficient. Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)