Overview
A computer vision research company developing an eye tracking AI platform partnered with Sourcefit to support data validation, gaze classification, and annotation consistency across model training workflows. The team required structured human review to evaluate gaze direction outputs, identify labeling issues, and correct inaccurate classifications. Sourcefit built a dedicated QA program to validate datasets and strengthen model improvement cycles.
The challenge
- Eye tracking outputs required accurate validation and classification
- Datasets included varied head positions, lighting conditions, and eye angles
- Inconsistent labels reduced training quality and limited model progression
- The client needed human reviewers aligned on classification criteria
- Calibration was necessary to ensure consistency across annotators
Our approach
Sourcefit built and operates a structured QA workflow supported by a trained team specializing in eye tracking review, visual pattern evaluation, and classification rules. The team validates model outputs by assessing gaze direction based on head orientation, eye positioning, and contextual cues.
During setup, Sourcefit:
- Conducted calibration sessions to align reviewers on gaze classification rules
- Created validation checklists for head alignment, eye angle, and confidence scoring
- Implemented multi step QA review to detect inaccurate or inconsistent labels
- Tracked reviewer accuracy and provided ongoing feedback to maintain consistency
- Supported model improvement cycles through daily dataset validation and documentation updates
Today, Sourcefit continues to provide ongoing dataset validation and structured QA support to strengthen model performance and improve training reliability.
Results
- Improved labeling accuracy across large eye tracking datasets
- Reduced inconsistencies driven by varied lighting, angles, and head movement
- Increased dataset reliability for training and testing cycles
- Supported faster iteration for model refinement and experiment runs
Key takeaways
- Clear classification rules improve dataset quality: Calibration and criteria alignment strengthen accuracy across complex visual datasets.
- Human validation strengthens reliability: Manual review captures subtle cues that automated systems often misclassify.
- Structured QA accelerates model improvement: Consistent validation workflows support faster iteration and refinement.
Industry learnings
Eye tracking systems rely on precise human judgment during early training stages. Slight variations in gaze, head angle, and lighting can disrupt model interpretation. Dedicated QA teams reinforce labeling accuracy, stabilize datasets, and support iterative improvement across computer vision pipelines.
Learn more
Sourcefit builds scalable QA and validation teams for AI, research, and computer vision organizations.
Explore WorkingAI for automation and dataset orchestration capabilities, or SourceCX for customer facing support programs.
Contact our AI operations team to explore structured validation and annotation support.
