Object Detection & Segmentation
Validate visual accuracy, uncover blind spots, and achieve pixel-perfect segmentation
Automatically identify missed detections, false positives, and segmentation drifts. Analyze failures, refine boundaries, and enhance model precision for real-world reliability.
Key Capabilities
Detect Missed and False Detections
Identify false positives, missed detections, and segmentation drift across image clusters.
Spot Vulnerabilities and Edge Cases
Detect class-wise vulnerabilities and under/over-segmented regions impacting accuracy.
Compare and Trace the Errors in data
Compare visual and model versions to trace boundary errors and performance regression.
Tests
An overview of all tests
Data Drift Detection
Identify scenarios in the field data which are drastically different (out-of-distribution) with respect to the training dataset. The AI model is prone to generating erroneous predictions on such datapoints.
This helps access if the data in the production setting has shifted and the model needs to be retrained.
Failure Mode Analysis
Identify scenarios where the model performs poorly on the test dataset post training/re-training.
This test helps users identify 90% of the vulnerabilities within a models Operational Design Domain (ODD) early in the model development lifecycle.
Outlier Detection
Monitor alterations in data distribution within a production environment.
This proactive test helps to ensure the model's accuracy and reliability over time.
Data Leakage Test
Detect instances of data leakage (non-independence) between the training and test datasets. If there is leakage, the model can artificially show very high performance on the test dataset.
This ensures that the model remains robust and can reliably generalize to new, unseen data without being influenced by leaked information.
Labelling Quality Test
Assess and enhance the accuracy of labelled data to improve model performance.
This helps enhance model training by providing high-quality labelled data, reducing biases and errors.
