AI Data Annotation for Food & E-commerce Image Classification
Worked on a large-scale data annotation project focused on food and e-commerce image datasets. Responsible for accurately labeling and categorizing thousands of images to train computer vision models used for product recognition, meal identification, and recommendation systems. Tasks included drawing bounding boxes around food items, classifying meals into predefined categories, and tagging images with relevant attributes such as ingredients, portion size, and presentation style. Maintained high accuracy and consistency across datasets by following strict annotation guidelines and quality control processes. Collaborated with cross-functional teams to refine labeling standards, improve dataset quality, and ensure alignment with model training objectives. This contributed to improved model performance in food detection and personalized recommendation systems.