INTRODUCING
Key to Precision and Efficiency in AI/ML Model Training
Unlock the Unpredictable World
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.
CREATE HIGH CONFIDENCE AI MODELS
Relentless Refinement
Selects uncertain and erroneous examples iteratively, ensuring your models learn from the most challenging scenarios.
Swift Automation
GENIE performs automatic labeling and synthesis of examples, streamlining the process and boosting efficiency.
Precision Enhancement
By targeting rare and complex data slices and classes, GENIE elevates accuracy to new heights.
Five To Ten Times Faster
What sets GENIE apart is its ability to label datasets 5 to 10 times faster than traditional methods, while also providing flexibility for continuous labeling and real-time model improvement. In short: it’s your ticket to unleashing the full potential of your robot team in dynamic environments.
Unlimited Use Cases
Case Study 1
Foreign Object Debris (FOD) Detection on Runways
GENIE revolutionized machine learning model training for complex tasks by minimizing labeled data needs, improving data quality through intelligent methods, saving substantial time/resources, and significantly enhancing model performance. It demonstrated great potential for efficient, accurate, and time-effective data labeling across applications.
Problem
Foreign Object Debris (FOD) on runways poses a critical safety risk in aviation. The goal was to develop an ML model that could effectively detect and classify 22 different classes of FOD on runways.
Solution
GENIE utilized four key functionalities to enhance the efficiency and accuracy of the data labeling process.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
Case Study 2
Defect Detection on Powerlines
This case study exemplifies GENIE’s capability to significantly streamline the training process for specialized machine learning tasks like insulator defect detection. By dramatically reducing labeled data needs and accelerating labeling, GENIE saved considerable time while ensuring high accuracy and efficiency. It underscores GENIE’s potential across sectors where rapid, efficient, and accurate data labeling is critical for timely and effective decision-making.
Problem
GENIE was employed to enhance the process of training a machine learning classifier for detecting
insulator defects on powerlines.
Solution
GENIE employed the following strategy to optimize the labeling process for training the insulator defect detection classifier.
Results
Model Performance: The classifier achieved high accuracy with 86% Mean Average Precision in
identifying insulator defects.
Case Study 3
Optimizing Sitting vs Standing Detection in a Temple Scenario with GENIE
GENIE revolutionized machine learning model training for complex tasks by minimizing labeled data needs, improving data quality through intelligent methods, saving substantial time/resources, and significantly enhancing model performance. It demonstrated great potential for efficient, accurate, and time-effective data labeling across applications.
Problem
The project focused on building a classifier to distinguish between sitting and standing postures in a temple. The primary challenge was gathering a diverse and accurate dataset for model training, a task that is both labor-intensive and time-consuming.
Solution
GENIE streamlined the dataset preparation for our classifier by blending manual and automatic labeling with data augmentation:
1. Manual Labeling: We started by manually labeling 150 varied and representative samples.
2. Automatic Labeling: GENIE then handled the labeling of an additional 1000 images, greatly cutting down on manual effort.
3. Data Augmentation: To enhance dataset diversity and model robustness, various data augmentation techniques were utilized.
Results
The project delivered impressive results:
* Model Performance: Achieved over 90% Mean Average Precision (MAP), accurately distinguishing between sitting and standing postures.
* Efficiency in Labeling: Our approach made labeling 15 times more efficient than traditional methods, drastically cutting down manual annotation.
* Time Savings:
* Labeling Time Reduction: Completed in just over an hour, a fraction of the time manual labeling would take.
* Rapid Deployment: The efficiency in labeling enabled swift model deployment, ensuring timely implementation in the temple context.
Case Study 4
Enhancing Obstacle Detection for Tethered Drones on Roads with GENIE
For tethered drones navigating roads, detecting obstacles scubas traffic signal poles, electric line, telephone poles, road signs, and bridges are crucial for a safe operation. Traditional methods of training obsticals detection models are often resource-intensie and time-consuming. GENIE was employed to optimize this process.
Problem
GENIE optimized the dataset preparation with a blend of methods:
1. Representative Sampling for Initial Labeling: 300 diverse samples were hand-labeled to represent various obstacle types.
2. Automatic Labeling: GENIE extended the dataset by labeling over 2000 additional images with minimal manual intervention.
3. Data Augmentation: Techniques like copy-paste augmentation were used to increase diversity and robustness, enhancing the model's real-world accuracy.
Solution
The project outcomes were notable:
* Model Performance: Achieved over 82% Mean Average Precision (MAP) for obstacle detection in tethered drones
* Labeling Efficiency: GENIE was 12 times more efficient than traditional methods, requiring only 300 hand-labeled samples.
* Time and Cost Savings:
* Labeling Time Reduction: Reduced from about 20 hours to 1.5 hours, over a 90% reduction.
* Overall Project Timeline: Cut total project time by 75%, enabling quicker deployment and operational readiness, with cost savings exceeding 85%.
Results
This case study highlights GENIE's transformative impact on training machine learning models for complex tasks, like obstacle detection in tethered drones. By drastically cutting manual labeling and improving dataset quality through advanced methods, GENIE achieved significant time and cost savings while maintaining high model accuracy and efficiency. This demonstrates GENIE's potential in sectors where rapid and precise data labeling is crucial for developing effective and timely technological solutions.
Case Study 5
Advancing Autonomous Driving with GENIE: Focusing on Rare and Challenging Scenarios
For tethered drones navigating roads, detecting obstacles scubas traffic signal poles, electric line, telephone poles, road signs, and bridges are crucial for a safe operation. Traditional methods of training obsticals detection models are often resource-intensie and time-consuming. GENIE was employed to optimize this process. Autonomous driving technology demands high accuracy in reverse and challenging scenarios, including Improvement in rare cases like detecting pedestrians and motorcyclists at night. Traditional approaches to training models for there scenarios are often inefficient, failing to adequately represent these rare but critical situations. GENIE was deployed to address this specific challenge in autonomous driving.
Problem
The challenge was to boost the model's accuracy in detecting pedestrians and motorcyclists at night on highways. These rare and challenging scenarios, often under-represented in datasets, are vital for the safety and reliability of autonomous driving systems.
Solution
GENIE improved model performance in difficult scenarios by:
1. Error Analysis: Identifying underperforming areas in the model.
2. Targeted Selection: Choosing samples from challenging and under-represented data slices for better training.
3. Efficient Use of Unlabeled Data: Using 1000 unlabeled samples to significantly boost accuracy in rare scenarios.
Results
The results were significant:
* Model Performance Improvement: Accuracy in challenging slices increased from under 30% MAP to over 80%.
* Overall Accuracy Enhancement: Improved overall model accuracy alongside gains in tough scenarios.
* Efficiency in Data Utilization: GENIE needed far fewer samples than traditional methods, potentially reducing the need for 100 times more samples.
* Time and Cost Savings:
* Reduction in Data Preparation: Fewer samples led to major time and cost reductions in data collection.
* Focused Training Effort: Targeted improvements made the training process more efficient, cutting down on time and resources.
Case Study 6
Enhancing Medical Imaging Analysis with GENIE
Medical imaging is a crucial domain where the precision of image analysis directly impacts diagnostic accuracy and patient care. Traditional image analysis often struggles with issues like long-tail imbalance due to rare classes and the presence of outliers that can skew results. GENIE was implemented to address these specific challenges in medical imaging.