New retail has become a major trend in AI applications. Facing a variety of new retail models, it is not easy to improve tremendously in essence. Based on AI computer vision algorithm, a cloth fashion company can directly recommend a client’s unique wear style according to gender, age, face shape, figure, clothing color, silhouette, foot shape and select the most suitable clothes and on the big screen inside the store.
The customized “face recognition” algorithm, combined with a store somatosensory camera, can quickly recognize customers’ facial features, establish personalized tags, and give customized clothing suggestions. As the customer enters the special experience area, the somatosensory camera in front of the customer will start work in real-time: locate and analyze the key points, and output the facial feature data report, including the face shape and eye shape.
Based on the image recognition training exercise and feature extraction of tens of millions of data, the clothing recognition algorithm can automatically recognize and output the clothing color, style, discovering the most suitable style for clients.
Now the algorithm can automatically detect the RGB value and proportion, recognize 8 kinds of silhouette categories such as H-type, V-type, X-type, and A-type, and associate 18 kinds of clothing styles such as commuting, simplicity, etc.
Data, Algorithms, and Processing are Three indispensable Elements of AI.
Data is the starting point.
It makes sense for AI to start with data and take advantage of learning itself. In scalable data and the high-speed era, it is very convenient to use data to train artificial intelligence.
The original data is generally acquired through data collection, and the subsequent data cleaning and data annotation are equivalent to processing the data, and then transferred to the Artificial intelligence algorithm and model for invocation.
If the data used in artificial intelligence training is not sufficiently diverse and unbiased, problems such as artificial “AI bias” may arise.
2D boxing，Polyline，Polygon，Image classification， Semantic segmentation，Video annotation
Goods on the shelf, goods picking-up
Object Tracking in Video:
Holding goods video annotation, personalized shopping
Cosmetics brand classification, clothing color style, and current popular style classification
Regional Segmentation :
Different areas segmentation
With real-time workflow management, providing flexible data training service for the machine learning industry.
Control Your Own Project
Clients can set labeling rules, iterate data features, attributes, and task flows, scale up or down, make changes, monitor the labeling progress, and get the results in real-time on the dashboard.
For example, you can choose a Polygon and Classification Template:
Or a One-step Classification Template:
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
We can provide personalized annotation tools and services according to customer requirements.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: firstname.lastname@example.org
1 Data Labeling — How to Select a Data Labeling Company
2 Labeling Service — Four Customer Pain Points in Getting No Bias Training Data?
3 No Bias Training Data — the New Bottlenecks in Machine Learning
4 Data Labeling Case Study in New Retail — Cosmetics Brand Classification and Labeling
5 What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?