Synthetic Data Generation for Livestock AI Training
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Solution Overview
Problem
Current methods for training Artificial Intelligence Systems (AIS) to recognize animals are inefficient, particularly when it comes to generating and controlling training data for animals farmed for financial gain, as they require extensive time and lack effective data generation methods.
Innovation Solution
A system and method that uses synthetic data to supplement real data, allowing for high control over parameters and rapid generation of training data, which is then used to train AIS, enabling quick and effective training of models for animal recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If captured real data is used to train AIS, then the training data reflects real animal variations, but the training process takes a long time and lacks control over data variations
Solution Approach 1:
The patent creates synthetic copies of real animal images through computer-generated imagery (CGI) that replicate the visual characteristics and variations of real animals. These synthetic copies serve as training data alternatives, maintaining representativeness while enabling faster generation and control over variations.
Solution Approach 2:
The system allows control over various parameters in synthetic image generation, such as animal size, color, lighting conditions, and background environments. By adjusting these parameters, the training data can be rapidly generated with controlled variations, reducing training time while maintaining data quality.
2Measurement precision
If more training data is generated to improve model accuracy, then the model performance improves, but the data generation and processing time increases
Solution Approach 1:
Synthetic image copies are generated rapidly using computer algorithms, creating large volumes of training data without the time constraints of capturing real images. This enables generation of extensive training datasets that improve model accuracy while maintaining fast production speeds.
Solution Approach 2:
The system pre-generates and stores synthetic training data with various controlled variations before actual training is needed. This preliminary preparation of diverse training data allows for immediate model training without time-consuming data collection during the training process.
3Productivity
If synthetic data is used to supplement real data, then training speed increases and control over parameters improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces a synthetic data generation system as an intermediary between real data capture and model training. This intermediary layer creates controlled synthetic versions of real images, enabling faster training with parameter control while managing system complexity through modular architecture.
Solution Approach 2:
The synthetic data generation system serves multiple functions: it creates training data, controls data variations, supplements real data limitations, and enables rapid experimentation. This multi-functionality justifies the added system complexity by providing comprehensive control over the training process.
Data Source
AI summary
A system and method are disclosed for training a system or a model to allow estimation of the value of livestock that is farmed for monetary gain. The various aspects of the invention include generation of data that is used to supplement or augment capture or real data, wherein the subject of the data is an animal. Labels or attributes are generated and validated.