YA's Blog

With the advent of AlexNet, all the submissions to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) switched over to using convolutional neural networks. In 2013, the winner of this challenge was ZFNet, a modified version of AlexNet which gave better accuracy. It was also an 8-layer network that tweaked some of the layer configurations of AlexNet by trial and error. ZFNet used 7 $\times$ 7 sized filters in the first layer with a stride of 2 instead of 11 $\times$ 11 filters with a stride of 4. The intuition behind this is that we were losing a lot of pixel information (aggressively downsampling the input), which can be retained by having smaller filter sizes and small strides. The padding is removed for the first two conv layers (to match the subsequent conv layers shapes of AlexNet). With just these two changes, they are able to achieve a reasonably large increase in performance over AlexNet. AlexNet or ZFNet were designed in somewhat an ad-hoc manner with s

Do you wonder about how to come up with different design choices (architecture, optimization method, data manipulation, loss function, etc.) for the deep learning model so that it gives the best performance? Let's look at the different CNN architectures that have performed well in the past on image classification tasks. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) was the huge benchmark for image classification because it held a yearly challenge from 2010 to 2017 where teams around the world would compete with their best-performing classification models. This competition uses a subset of ImageNet's images containing 1.2 million high-resolution images with 1000 different classes and challenges researchers to achieve the lowest top-1 and top-5 error rates (top-5 error rate would be the percent of test images where the correct label is not one of the model's five most likely labels). For the first two years (2010 and 2011), the winning systems were