Independent Image RLHF Portfolio
Developed an independent Image RLHF (Reinforcement Learning with Human Feedback) portfolio focused on evaluating and ranking AI-generated images for model alignment and quality improvement. Assessed outputs based on prompt alignment, visual realism, and technical accuracy, identifying defects such as anatomical inconsistencies, distortion, blur, and artifacts. Applied a structured evaluation framework to ensure consistent and high-quality annotation across datasets. Produced detailed written justifications for each decision, including handling ambiguous and edge-case scenarios, to support model training and refinement. Maintained organized documentation of evaluation outcomes using a systematic workflow to simulate real-world AI training environments. - Ranked images using a structured hierarchy: prompt alignment → technical quality → aesthetics - Identified and annotated visual defects (anatomy errors, distortion, blur, artifacts) - Provided clear, structured justifications for model improvement - Handled edge cases and ambiguous outputs with consistent reasoning - Maintained organized documentation using tools like Google Sheets and Excel