Fashionpedia: The Visual Dictionary of Fashion Design

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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. author={Jia, Menglin and Shi, Mengyun and Sirotenko, Mikhail and Cui, Yin and Cardie, Claire and Hariharan, Bharath and Adam, Hartwig and Belongie, Serge} The results format is similar to COCO format for object detection with additional attribute_ids filed. See evaluation demo and also loadRes() in Fashionpedia API.

FASHIONPEDIA improves the productivity of fashion designers as it serves as a fashion archive for brainstorming ideas and at the same time a dictionary for all the technical terms to communicate with the development departments. 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 We designed FASHIONPEDIA with productivity and efficiency in mind. Designers can easily generate ideas by browsing through thousands of items and details in the book. At the same time, communication within the fashion industry will become seamless and stress-free as designers have all the technical terms in their hands. A novel task of fine-grained instance segmentation with attribute localization. The proposed task unifies instance segmentation and visual attribute recognition, which is an important step toward structural understanding of visual content in real-world applications.

Additionally, we also provide metrics with only IoU constraint and only F1 thresholds constraint, for better understanding of the algorithm. See evaluation demo for more details. Result format We focus on presenting information using the most practical mindset possible, making knowledge easy to digest and apply.

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.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

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 48k everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. Hashes for fashionpedia-1.1-py3-none-any.whl Hashes for fashionpedia-1.1-py3-none-any.whl Algorithm 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. A visual fashion dictionary covering all the technical terms from style to material to production with illustrations and infographics.

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Textilepedia covers Fibers, Yarn, Weave, Knits, Lace & Netting, Non-woven & Felting, Hides, Finishings, Patterns & Colors. The manual covers branding, product development, wholesaling, retailing, setting up your business and form templates. To ensure a strong fabrication foundation, knowledge of textiles is key. Not only is it the functional base of all your designs, but the stronger your understanding of textiles, the more you will be able to push the boundaries of creativity. 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. 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).