An image classifier using a pre-trained ResNet-50 architecture

ResNet-50 stands for Residual Network, which is a type of CNN architecture that was first published in a computer vision research paper entitled Deep Residual Learning for Image Recognition, by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, in 2015.

ResNet is currently the most popular architecture for image-related tasks. While it certainly works great on image classification problems (which we will see as follows), it works equally great as an encoder to learn image representations for more complex tasks such as Self-Supervised Learning. There are multiple variations of ResNet architecture, including ResNet-18, ResNet-34, ResNet-50, and ResNet-152 based on the number of deep layers it has.

The ResNet-50 architecture has 50 deep layers and is trained on the ImageNet dataset, which has 14 million images belonging to 1,000 different classes, including animals, cars, keyboards, mice, pens, and pencils. The...