“New Retail” is the term Alibaba uses to describe the blending of online and offline commerce through the digitization of the entire retail value chain for the benefit of both the merchant and the consumer and, of course, the company enabling this transformation.
The retail industry is a labor-intensive industry, among all the infrastructures, the cashier labor cost occupies a considerable part.
With the development of deep learning, it is an inevitable trend to use image recognition technology to achieve cost reduction while increasing efficiency.
At present, a popular intelligent solution is visual recognition, which takes image recognition as the core technology, and takes camera, and mainboard as the core hardware. It conducts object detection and classification, realizes automatic recognition and settlement, improving the shopping experience, and saving labor costs.
The solution is already commercialized in some regions. However, in reality, some problems emerged, mainly on the accuracy issue. For example, the package doesn’t vary from each other, which makes recognition more difficult.
Just as a triangle needs three sides to stabilize its shape, artificial intelligence will also need all three elements to perfect itself: algorithm, no bias data sample, and the training process.
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It is well known that the performance of an AI model depends more on the data than the code. Getting high-quality labeled data is the toughest part of building a machine learning model. If the training data has a bias, the algorithm model cannot be well developed, AI company needs to label the data again. Timing is important, once the company is behind the schedule, the product may be overtaken by competitors.
2D boxing，Polyline，Polygon，Image classification，Semantic segmentation，Video annotation
Data Type: goods on the shelf, goods picking-up, different areas segmentation, holding goods video annotation
- Product Classification: cosmetics brand classification, clothing color style, and current popular style classification
Specialized and customized scenarios are the main development trend of the data annotation industry. High-quality data sets will effectively improve the accuracy of image recognition and bring new vitality to the commercialization of the new retail industry.
ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.
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.
These labeling tools are available on the dashboard :Image Classification, 2D boxing, Polygon, Cuboid
For more information, please visit our website:ByteBridge.io