Pizza Toppings Object Detection
Pizza Toppings Object Detection
Datasets
Pizza Toppings Object Detection
File
Pizza Toppings Object Detection
Use Case
Pizza Toppings Object Detection
Description
Annotated pizza dataset sourced 9000 images, meticulously labeled with 16 ingredients like Cheese, Pepperoni, and Basil. Using RoboFlow, the dataset was split into training, validation, and testing subsets, with augmentations applied to enhance model training.
Description:
The object detection process began with an annotated pizza dataset sourced from a Kaggle repository, containing approximately 9000 pizza images that showcased diverse visual conditions and angles. From this collection, about 1500 images were randomly selected and meticulously annotated with 16 ingredient labels, including common pizza components like Cheese, Pepperoni, and Basil. The annotation process was streamlined using RoboFlow, a versatile dataset creation tool that facilitated label management, bounding box creation, and image sorting. The dataset was then split into training, validation, and testing subsets in a 60%, 20%, and 20% ratio respectively, ensuring a comprehensive evaluation framework.
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To enhance the training set, augmentations such as rotation and blur were applied, increasing its size to 2544 images, while the validation and testing sets contained 284 and 283 images respectively. This extensive preparation and augmentation laid a solid foundation for the subsequent model training and evaluation phases. RoboFlow’s visual aids provided valuable insights into dataset characteristics, including label representation and object placement within images, contributing to a more effective and insightful annotation process.
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