The paper introduces UnitBox, an advanced object detection network that addresses the limitations of existing deep CNN methods in object localization. Traditional methods often assume that the four bounds of a bounding box are independent variables, which can lead to less accurate localization. To improve this, the authors propose a novel Intersection over Union (IoU) loss function, which regresses the four bounds as a whole unit, enhancing the accuracy and efficiency of localization. UnitBox leverages deep fully convolutional networks and the IoU loss to achieve robust performance on objects of varied shapes and scales, with fast convergence. The network is applied to face detection tasks, achieving the best performance among all published methods on the FDDB benchmark. The IoU loss layer is compared with the widely used $\ell_2$ loss, demonstrating superior performance in both accuracy and efficiency. UnitBox's architecture is derived from the VGG-16 model, with additional layers for confidence and bounding box prediction, and it shows significant improvements in both training speed and detection accuracy.The paper introduces UnitBox, an advanced object detection network that addresses the limitations of existing deep CNN methods in object localization. Traditional methods often assume that the four bounds of a bounding box are independent variables, which can lead to less accurate localization. To improve this, the authors propose a novel Intersection over Union (IoU) loss function, which regresses the four bounds as a whole unit, enhancing the accuracy and efficiency of localization. UnitBox leverages deep fully convolutional networks and the IoU loss to achieve robust performance on objects of varied shapes and scales, with fast convergence. The network is applied to face detection tasks, achieving the best performance among all published methods on the FDDB benchmark. The IoU loss layer is compared with the widely used $\ell_2$ loss, demonstrating superior performance in both accuracy and efficiency. UnitBox's architecture is derived from the VGG-16 model, with additional layers for confidence and bounding box prediction, and it shows significant improvements in both training speed and detection accuracy.