For object detection, annotated dataset is necessary, and the extensive amount of time required in making such dataset is a monumental task. Conventionally, to obtain a highly precise object detection model, it was essential to investigate target features of the object and develop a feature extraction capable of expressing it, but providing a model through such task was challenging. Furthermore, application depends on users, and creating a high-precision model based solely on its intended use was difficult. Moreover, fulfilling the demand for object detection models in a niche market was another major challenge. To overcome these issues, GRID developed “ReNomTAG”, a web application which can create the required annotate dataset for object detection and “ReNom IMG”, which can take dataset from “ReNom TAG” allowing one to create, compare, predict, and evaluate a model. With these two applications, working on object detection models become easier and the monumental time required to create a model reduces.