CNN based Dog Breed Classifier Using Stacked Pretrained Models

In this article, we will learn how to classify images based on fine details of images using a stacked pre-trained model to get maximum accuracy in TensorFlow. Hey folks, I hope you have done some image classification using pre-trained TensorFlow or TensorFlowor other CNN pre-trained models and might have some idea about how we classify images, but when it comes to classifying finely detailed objects (dog breed, cat breed, leaves diseases) this method doesn't give us a good result, in this case, we would prefer model stacking to capture most of the details. Let's get straight to the technicalities of it. In our dataset, we have 120 dog breeds and we will have to classify them using a stacked pre-trained model (TensorFlow, Densenet121) which is trained on Imagenet. We will stack bottleneck features extracted by these models for greater accuracy that will depend on the models we are stacking together.