AI for virtual fitting: inspired by datasets (Open & Commercial)

  • Posted by maadaa
  • October 21, 2022
  • Updated 4:27 pm
Person And Clothes Semantic Segmentation Dataset

KEYWORDS:#Virtual-Fitting   #Open-Datasets   #WAB   #Deep-Fashion-3D   #Fashion-Datasets    #Clothing-Classification-Dataset    #Clothing-Keypoints    #Person&Clothes-Segmentation    #Clothes-Segmentation   #Human-Body-Segmentation   #maadaa.ai 

According to Fit Small Business, the virtual fitting market was worth $2.97 billion in 2021 and is forecast to grow to more than $8.5 billion by 2028. 

So,  Is 2023 the year for your AI products or services to start engaging the virtual fitting technology?

High quality and large-volume datasets are inspiring the innovations like virtual fitting. Here we summarized state-of-the-art open and commercial datasets behind the enabling technologies. 

👉👉 AI for virtual fitting: application scenarios and enable technologies

1. Open datasets

 

1.1 Watch and Buy (WAB)

The WAB dataset was collected by Alibaba from taobao daily clothing and live streaming. There were 1,042,178 labeled images, 1,654,780 labeled detection frame instances, and 70,000 transcribed labeled video texts.

The data annotated 23 clothing detection categories and detection frame positions, which can be used for object detection algorithm research. Box-level instance numbers are annotated in the data, and about 80,000 groups of commodity sequences of the same type are constructed, which can be used for object retrieval and recognition algorithm research.

Watch and Buy (WAB)

Links: https://tianchi.aliyun.com/competition/entrance/531893/information 

 

1.2 Deep Fashion3D

This dataset is the largest 3D scanning dataset available. Deep Fashion3D[7] contains 2000 scanning 3D models, and each model contains rich annotations, such as ground-truth point cloud annotations, multi-angle real pictures, and 3D body pose annotations, including ten clothing categories.

Deep Fashion3D datasets

Links: https://github.com/kv2000/deepFashion3D

 

2. Fashion dataset of Maadaa.ai

Based on the accumulation of the Fashion technology and application field by Maadaa.ai, we summarized the typical application scenarios of Fashion, designed and completed several datasets .

 

2.1 Clothing Classification Dataset (MD-Fashion-1)

Md-fashion-1 is the dataset of clothing classification. There are about 200W images, data collected from e-commerce, fashion shows, social media and other scenes. The annotation method of the dataset adopts image category annotation and BBox annotation, covering a total of 80 category tags including different clothing styles and scenes. 

Clothing Classification Dataset 

Links: https://maadaa.ai/dataset/clothing-classification-dataset/ 

 

2.2 Clothing Keypoints Dataset (MD-Fashion-5)

Md-fashion-5 is a dataset of clothing key points, containing 100W pictures. The dataset covers 80 clothing types with the coordinates of key points and Bbox as annotated information.

MD-Fashion-5 

Links: https://maadaa.ai/dataset/clothing-keypoints-dataset/ 

 

2.3 Person And Clothes Semantic Segmentation Dataset (MD-Image-026)

Md-image-026 splits datasets for people and clothing. The dataset contains 19.7w images with a minimum resolution of 92 x 153 and a maximum resolution of 3024x 5381. Clothing categories include background, hat, hair, sunglasses, coat, skirt, pants, hat, gloves, sunglasses, coat, socks, skirt, shoes and body parts such as face, left and right legs, left and right arms, etc. Compared with MD-image-027, MD-image-026 adds more semantic segmentation categories of body parts, such as face, etc.

Person And Clothes Semantic Segmentation Dataset

Links: https://maadaa.ai/dataset/person-and-clothes-semantic-segmentation/ 

 

2.4 Clothes Segmentation Dataset (MD-Image-027)

Md-image-027 is mainly a clothing segmentation dataset collected from the Internet. The dataset contains 1.43w images with resolutions between 183 x 275 and 3024 x 4032. Through pixel level segmentation semantic annotation of background, hat, hair, sunglasses, coat, skirt, pants, dress, tie, left shoe, right shoe, face, left leg, right leg, left arm, right arm, bag, scarf, mobile phone and other large accessories, a total of about 30 target categories, making the dataset in e-commerce, Many scenes such as visual entertainment and metasurverse virtual human have important application value.

Clothes Segmentation Dataset

Links: https://maadaa.ai/dataset/clothes-segmentation/ 

 

2.5 Human Body Parts Fine Segmentation Dataset (MD-Video-005)

MD-Video-005, diversified scenes such as dancing, talent shows, movies, TV stories. Includes 19 categories: background, face, hair, top, left arm, right arm, trousers, left leg, right leg, skirt, left shoe, right shoe, bag, etc.

Human Body Parts Fine Segmentation Dataset

Links: https://maadaa.ai/dataset/high-precision-human-body-segmentation/ 

 

2.6 Human Body Segmentation Dataset (MD-Image-016)

MD-Image-016, a large division of the human body including the human body, arms, hands, background or background, obstructions, hair, Sunglasses + glasses, and skin in different areas.

Human Body Segmentation Dataset

Links: https://maadaa.ai/dataset/human-body-segmentation-2/ 

 

Further Reading:

👉 Face Parsing: use cases and open datasets

👉 Video face segmentation: use cases and open datasets

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