Image Classification for Fruit
OtherImageClassificationObject Detection
I participated in a supervised computer vision project focused on image classification. The goal was to train an AI model to recognize and classify the ripeness state of fruit based on images. We created and labeled a custom image dataset, manually assigning class labels such as unripe, ripe, and rotten to fruit images (e.g., bananas).
My role involved preparing and labeling the dataset, ensuring label consistency, and evaluating model predictions against ground truth data. The project emphasized data quality, correct class definitions, and validation of outputs to improve classification accuracy. This experience closely mirrors real-world data labeling and AI training workflows used in computer vision systems.
I participated in a supervised computer vision project focused on image classification. The goal was to train an AI model to recognize and classify the ripeness state of fruit based on images. We created and labeled a custom image dataset, manually assigning class labels such as unripe, ripe, and rotten to fruit images (e.g., bananas). My role involved preparing and labeling the dataset, ensuring label consistency, and evaluating model predictions against ground truth data. The project emphasized data quality, correct class definitions, and validation of outputs to improve classification accuracy. This experience closely mirrors real-world data labeling and AI training workflows used in computer vision systems.
2024 - 2025