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 5
Advancing Autonomous Driving with GENIE: Focusing on Rare and Challenging Scenarios
Background
Autonomous driving technology demands high accuracy in diverse and challenging scenarios, including Improvement rare cases like detecting pedestrians and motorcyclists at night. Traditional approaches to training models for these scenarios are often inefficient, failing to adequately represent these rare but critical situations. GENIE was deployed to address this specific challenge in autonomous driving.
Challenge
The primary challenge was to enhance the model’s performance in rare and difficult scenarios, such as detecting pedestrians and motorcyclists at night on highways. These scenarios, which are under-represented in typical datasets, are crucial for the safety and reliability of autonomous driving systems.
GENIE’s Approach
GENIE’s strategy focused on targeted selection and error analysis to improve model performance in these challenging scenarios.
- Error Analysis: GENIE analyzed the model’s performance to identify hard and problematic scenarios where the model was underperforming.
- Targeted Selection: Based on the error analysis, GENIE used targeted selection to choose samples from under-represented and challenging slices of data, ensuring that these critical scenarios were adequately represented in the training data.
- Efficient Use of Unlabeled Data: GENIE leveraged just 1000 unlabeled samples to significantly improve the model’s accuracy in these rare scenarios.