AI Training Platform With Unified Annotation and Model Conversion
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Solution Overview
Problem
Conventional AI model training systems are closed-source, limited by specific data annotation conventions and input file formats, restrictive in configuration and optimization, and compatible with only a small proportion of available model/engine output formats and third-party plugins.
Innovation Solution
A unified training platform that unifies preprocessing, configuration, training, and evaluation of multiple neural network-based object detection algorithms, providing a neural network-agnostic environment with unified data annotation formatting and full accessibility to network optimizations, including a universal model converter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional closed-source systems are used for AI model training, then system stability is maintained, but adaptability and versatility are limited due to restrictions on custom integrations and optimizations
Solution Approach 1:
The patent implements a universal training platform that supports multiple neural network architectures (YOLO, SSD, Faster R-CNN, etc.), data formats (JSON, XML, CSV, YAML), and output formats through a unified interface. This multi-functional design allows the system to adapt to various object detection algorithms and data conventions without requiring separate training systems for each case, thereby improving adaptability while maintaining manageable complexity through standardization
2Adaptability or versatility
If conventional systems with specific data annotation conventions are used, then training consistency is ensured, but adaptability to different data formats is reduced
Solution Approach 1:
The patent introduces format conversion modules that act as intermediaries between diverse data annotation formats and the internal processing requirements of the training system. These converters translate JSON, XML, CSV, and YAML formats into a unified internal representation, enabling the system to handle multiple formats without compromising operational simplicity or training consistency
3Adaptability or versatility
If conventional systems with limited model format support are used, then system simplicity is maintained, but versatility in deploying different model formats is restricted
Solution Approach 1:
The patent implements universal model conversion capabilities that support multiple output formats (ONNX, TensorRT, OpenVINO, TensorFlow Lite, PyTorch) through a unified conversion framework. This allows the system to deploy trained models across diverse hardware platforms and production environments without requiring separate training pipelines for each target format, thereby enhancing versatility while managing conversion complexity through standardized processes
Data Source
AI summary
Systems, methods, apparatuses and non-transitory computer executable media configured to unify preprocessing, configuration, training, monitoring, and evaluation of multiple neural network based object detection algorithms under a singular development environment/platform (i.e., a “unified training platform”). The unified training platform may include a neural network agnostic model training environment that may allow for unified data annotation formatting. In addition to incorporating a wide variety of state-of-the-art neural networks into the unified training platform, the unified training platform may also provide full accessibility to available network optimizations. The unified training platform may also include a universal model converter.