CNN Apparel Attribute Recognition for Retail Planning
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Solution Overview
Problem
Current retail planning systems rely heavily on manual processes for generating product assortments and identifying apparel and attributes, which is inefficient and cannot scale for large product inventories and real-time scenarios.
Innovation Solution
The implementation of an automatic image-based recognition system using deep learning models to identify products and granular attributes from various media types, reducing the need for manual input and enhancing data interpretation from multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for generating product assortments and identifying apparel attributes, then system complexity is low, but productivity is low and time consumption is high
Solution Approach 1:
The patent replaces manual mechanical processes with an automated image-based recognition system using deep learning models. The system automatically detects apparel items and extracts granular attributes from images, videos, and other media, eliminating the need for manual data entry and significantly improving productivity while accepting increased system complexity through the use of AI/ML infrastructure.
Solution Approach 2:
The system enables self-service by automatically processing media files and generating product attribute data without human intervention. The deep learning models autonomously perform detection, classification, and attribute extraction tasks, allowing the system to serve itself in processing large volumes of product information at scale.
2Productivity
If automated apparel detection systems are used, then productivity improves, but measurement precision of granular attributes deteriorates
Solution Approach 1:
The patent segments the attribute detection process into multiple specialized deep learning models, each trained to detect specific granular attributes (e.g., neckline type, sleeve length, pattern, fabric texture). This segmentation allows each model to focus on specific features, improving measurement precision while maintaining high processing throughput through parallel operation of multiple specialized detectors.
Solution Approach 2:
The system transitions from traditional 2D image analysis to multi-dimensional attribute space by extracting granular features across different dimensions (visual appearance, fit characteristics, fabric properties). This dimensional expansion enables more precise measurement of attributes while maintaining productivity through efficient multi-dimensional feature extraction algorithms.
3Loss of time
If manual data entry is required for product information, then data accuracy can be controlled, but loss of time increases and productivity decreases
Solution Approach 1:
The system implements feedback mechanisms where detected attributes are continuously validated and refined. The deep learning models provide confidence scores for each detected attribute, and the system uses this feedback to prioritize manual review only for low-confidence detections, thereby minimizing time loss while maintaining high data accuracy through automated validation loops.
Data Source
AI summary
A system and method of automatic product attribute recognition receive training images having bounding boxes associated with one or more products in the training images, receive attribute values for each of the one or more products in the training images, and train a first convolutional neural network (CNN) model to generate bounding boxes for and identify each of the one or more products with the training images until the accuracy of the first CNN model is above a first predetermined threshold. The system and method further train a second CNN model for each of the products associated with the cropped images until the second CNN generates attribute values for the one or more attributes with an accuracy above a second predetermined threshold, and automatically recognize the one or more attributes for a new product image by presenting the product image to the first and second CNN models.