Model Compression: needs and importance

Whether you're new to computer vision or an expert, you've probably heard about AlexNet winning the ImageNet challenge in 2012. That was the turning point in computer vision history because it showed that deep learning models can perform tasks which were considered very difficult for computers, with an unprecedented level of accuracy. But did you know that AlexNet had 62 million trainable parameters? Another popular model VGGNet which came out in 2014 had even more, 138 million trainable parameters. You might be thinking… I know that the deeper the model is, the better it will perform.