Holdout Set

In the field of computer vision, a Holdout Set refers to a portion of data separated from the original dataset, used to evaluate the model's generalization capability on unseen data. The primary goal is to provide an independent testing environment to ensure the objectivity and reliability of the model's performance. By using a Holdout Set, researchers can effectively detect overfitting, optimize model parameters, and enhance the robustness and predictive accuracy of the model. The application value of the Holdout Set lies in its ability to offer critical reference points for model selection and validation, thereby promoting the development and practical application of computer vision technology.