This paper presents a novel architecture for efficient object localization using Convolutional Networks (ConvNets) to improve the accuracy of human joint localization. Traditional ConvNet architectures, while effective for classification tasks, suffer from reduced localization accuracy due to the use of pooling layers. The authors introduce a 'position refinement' model that estimates joint offsets within small image regions, trained in cascade with a state-of-the-art ConvNet model. This approach achieves improved accuracy in human joint location estimation, outperforming existing methods on the FLIC and MPII-human-pose datasets. The paper also discusses the impact of pooling on spatial precision and introduces a modified dropout method called SpatialDropout to enhance generalization performance. The results demonstrate that the proposed model can achieve high spatial accuracy while maintaining computational efficiency.This paper presents a novel architecture for efficient object localization using Convolutional Networks (ConvNets) to improve the accuracy of human joint localization. Traditional ConvNet architectures, while effective for classification tasks, suffer from reduced localization accuracy due to the use of pooling layers. The authors introduce a 'position refinement' model that estimates joint offsets within small image regions, trained in cascade with a state-of-the-art ConvNet model. This approach achieves improved accuracy in human joint location estimation, outperforming existing methods on the FLIC and MPII-human-pose datasets. The paper also discusses the impact of pooling on spatial precision and introduces a modified dropout method called SpatialDropout to enhance generalization performance. The results demonstrate that the proposed model can achieve high spatial accuracy while maintaining computational efficiency.