Implementing a fully convolutional network (FCN) in TensorFlow 2

Using a pre-trained model that is trained on huge datasets like ImageNet, COCO, etc. we can quickly specialize these architectures to work for our unique dataset. This process is termed as transfer learning. Pre-trained models for image classification and object detection tasks are usually trained on fixed input image sizes. These typically range from 224x224x3 to somewhere around 512x512x3 and mostly have an aspect ratio of 1 i.e. the width and height of the image are equal. If they are not equal then the images are resized to be of equal height and width.