This research explores how making a neural network deeper (which means adding more layers) affects its ability to recognize and categorize large-scale images. Specifically, it looks at convolutional neural networks (ConvNets), which are a type of AI particularly good at processing visual information.In simpler terms, think of a ConvNet as a multi-layered system that looks at an image and tries to understand different aspects of it at each layer. For instance, the first few layers might identify edges and colors, while deeper layers might recognize complex patterns like shapes or even whole objects.The unique aspect of this study is the use of very small filters, sized 3x3, in these layers. Filters are like tiny lenses that focus on specific parts of an image to extract features. The researchers found that using these small filters in a deep network (with 16 to 19 layers of weights) significantly improves the network's accuracy in recognizing and categorizing images.Their findings were so impactful that they led to their team winning first and second places in the localization and classification tracks, respectively, of the 2014 ImageNet Challenge – a major competition in computer vision.Additionally, they demonstrated that their approach isn't just good for the specific images they trained on; it can be applied to other datasets and still achieve state-of-the-art results. The significance of this work is so high that they made their best-performing models available to the public, encouraging further research in the field.In summary, this study shows that deeper networks with small filters can be incredibly effective in understanding and recognizing images, a major step forward in the field of computer vision.