In this study, we explore how the depth of convolutional networks impacts their accuracy
 in large-scale image recognition tasks. Our key contribution is a comprehensive evaluation 
of increasingly deeper networks, utilizing an architecture with very small (3x3) convolution 
filters. We demonstrate that by extending the depth to 16-19 weight layers, a notable improvement 
over previous configurations can be achieved. These insights formed the foundation for our 
submission to the ImageNet Challenge 2014, where our team won first and second places in 
the localization and classification tracks, respectively. Additionally, we show that the representations 
learned by our model generalize effectively to other datasets, where they deliver state-of-the-art 
performance. To support further research on the use of deep visual representations in computer 
vision, we have made our top two performing ConvNet models publicly accessible.