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 2
Streamlining Insulator Defect Detection on Powerlines with GENIE
Background
Detecting insulator defects on powerlines is crucial for maintaining the integrity and safety of power distribution systems. Traditional methods of defect detection, often manual and time-consuming, can lead to inefficiencies and delayed responses. GENIE was employed to enhance the process of training a machine learning classifier for this purpose.
Challenge
The objective was to develop a classifier capable of distinguishing between three classes of insulator conditions: Damage, Broken, and No Issues. The challenge lay in accurately labeling a sufficient number of images to train the classifier effectively, a process typically demanding considerable time and resources.
GENIE’s Approach
GENIE’s strategy involved a combination of active selection for labeling, automatic labeling, and data augmentation to optimize the labeling process.
- Active Selection for Labelling: Initially, 1000 images were labelled using active selection, focusing on the most informative and diverse examples to train the model.
- Automatic Labelling: GENIE automatically labelled an additional 2000 images, leveraging its advanced algorithms to reduce manual effort significantly.
- Data Augmentation: The dataset was further enhanced through data augmentation techniques, improving the model’s ability to generalize from limited examples.
For results in model performance and demonstration of GENIE in the application of Streamlining Insulator Deficit Detection on Powerlines with GENIE Subscribe: