This paper presents a novel approach to object detection using deep convolutional neural networks (DCNNs). The proposed method, called DeepMultiBox, predicts multiple bounding boxes and confidence scores for each box, allowing for efficient detection of multiple instances of the same object. The model is trained to handle a variable number of instances per class and generalizes well to unseen classes. The authors define object detection as a regression problem and use a loss function that optimizes the matching between predicted and ground-truth boxes while maximizing the confidence scores of the matched boxes. The method is evaluated on the Pascal Visual Object Classes (VOC) 2007 and ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 2012 datasets, achieving competitive performance with a small number of predicted locations and neural network evaluations. The results demonstrate the scalability and effectiveness of the DeepMultiBox approach in object detection tasks.This paper presents a novel approach to object detection using deep convolutional neural networks (DCNNs). The proposed method, called DeepMultiBox, predicts multiple bounding boxes and confidence scores for each box, allowing for efficient detection of multiple instances of the same object. The model is trained to handle a variable number of instances per class and generalizes well to unseen classes. The authors define object detection as a regression problem and use a loss function that optimizes the matching between predicted and ground-truth boxes while maximizing the confidence scores of the matched boxes. The method is evaluated on the Pascal Visual Object Classes (VOC) 2007 and ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 2012 datasets, achieving competitive performance with a small number of predicted locations and neural network evaluations. The results demonstrate the scalability and effectiveness of the DeepMultiBox approach in object detection tasks.