INTRODUCING
Key to Precision and Efficiency in AI/ML Model Training
In the dynamic realm of machine learning (ML) and Artificial Intelligence (AI), GENIE emerges as a trailblazing tool by AvaWatz, crafted to redefine the approach to AI training. This comprehensive solution tackles the fundamental challenges in preparing datasets for machine learning, offering a suite of features that enhance efficiency, accuracy, reliability, and trustworthiness in ML model training.
Case Study 3
Optimizing Sitting vs Standing Detection in a Temple Scenario with GENIE
Background
In certain religious organizations, managing the congregation’s movement, specifically distinguishing between sitting and standing behaviors, is crucial for maintaining decorum and adhering to specific practices. Traditional methods of monitoring and enforcing these rules can be challenging and resource-intensive. GENIE was utilized to develop a machine learning model to automate this process efficiently.
Challenge
The goal was to create a classifier to accurately differentiate between sitting and standing postures in a temple environment. The challenge involved obtaining a sufficiently diverse and accurate dataset for training the model, typically a time-consuming and labor-intensive task.
GENIE’s Approach
GENIE’s strategy combined minimal manual labeling, extensive automatic labeling, and data augmentation to optimize the dataset preparation process.
- Manual Labeling: Initially, only 150 samples were manually labeled, focusing on selecting the most representative and varied examples.
- Automatic Labeling: GENIE then automatically labeled an additional 1000 images, significantly reducing the manual effort required.
- Data Augmentation: To enrich the dataset’s diversity and improve the model’s robustness, various data augmentation techniques were employed.