Image Generation Evaluation & AI-Generated Image Assessment Contributor
I contributed to an image data annotation project for Turing as a freelance/contract worker. The project supported multimodal AI model development, with a strong emphasis on visual assessment tasks. I conducted detailed image quality checks and prompt adherence evaluations on diverse image datasets. This included carefully reviewing images for overall visual quality (sharpness, realism, absence of artifacts, composition, and aesthetic appeal) and verifying how accurately each image followed the corresponding text prompt or description. The scope involved assessing roughly 5,000–20,000 images across various domains such as general objects, scenes, and UI elements. I strictly followed detailed evaluation guidelines, performed self-reviews for consistency, participated in calibration sessions using gold-standard examples, and incorporated QA feedback and AI-assisted tools to achieve high accuracy (95%+ inter-annotator agreement).