This paper presents a novel approach for scalable object detection using deep neural networks. The proposed method, called DeepMultiBox, predicts multiple bounding boxes simultaneously, which represent potential object locations. The model uses a deep neural network (DNN) to generate these boxes in a class-agnostic manner, allowing it to handle a variable number of instances for each class and generalize across different object categories. The model outputs a confidence score for each bounding box, indicating the likelihood that it contains an object of interest. This approach allows for efficient detection of a large number of object classes and is capable of generalizing to unseen classes. The key contributions of the paper include formulating object detection as a regression problem to the coordinates of several bounding boxes, defining a loss function that trains the bounding box predictors as part of the network training, and training the model in a class-agnostic manner. The model is able to achieve competitive recognition performance on the VOC2007 and ILSVRC2012 benchmarks, while using only a small number of predicted locations per image and a limited number of neural network evaluations. The proposed method is evaluated on two challenging benchmarks: VOC2007 and ILSVRC2012. The results show that the DeepMultiBox approach is competitive with other state-of-the-art methods and is able to generalize across different object categories. The model is also able to detect multiple instances of the same object class, which is an important feature for algorithms aiming to achieve better image understanding. The method is scalable and can be applied to a wide range of object detection tasks. The paper concludes that the proposed approach is a promising solution for scalable object detection using deep neural networks.This paper presents a novel approach for scalable object detection using deep neural networks. The proposed method, called DeepMultiBox, predicts multiple bounding boxes simultaneously, which represent potential object locations. The model uses a deep neural network (DNN) to generate these boxes in a class-agnostic manner, allowing it to handle a variable number of instances for each class and generalize across different object categories. The model outputs a confidence score for each bounding box, indicating the likelihood that it contains an object of interest. This approach allows for efficient detection of a large number of object classes and is capable of generalizing to unseen classes. The key contributions of the paper include formulating object detection as a regression problem to the coordinates of several bounding boxes, defining a loss function that trains the bounding box predictors as part of the network training, and training the model in a class-agnostic manner. The model is able to achieve competitive recognition performance on the VOC2007 and ILSVRC2012 benchmarks, while using only a small number of predicted locations per image and a limited number of neural network evaluations. The proposed method is evaluated on two challenging benchmarks: VOC2007 and ILSVRC2012. The results show that the DeepMultiBox approach is competitive with other state-of-the-art methods and is able to generalize across different object categories. The model is also able to detect multiple instances of the same object class, which is an important feature for algorithms aiming to achieve better image understanding. The method is scalable and can be applied to a wide range of object detection tasks. The paper concludes that the proposed approach is a promising solution for scalable object detection using deep neural networks.