This is in continuation of the last post where I scraped images from Google Search. In this part we will see the training of the model using Neural Networks with images as input. For training the model, I have utilised the Google Colab environment because training Neural Networks for image processing is memory intensive. Google Colab provides free GPU access for faster processing making it ideal for developers like me. The training data was uploaded on the drive manually.

In this project, we will be classifying the breed of the dog from the given photo of a dog as input. The dataset consists of 5 selected different breeds of dogs. Each folder is named after a breed and contains around 120 images of that breed. Based on the given image, we need to classify the breed as one of the 5 breeds present. Hence, we have trained a sequential model to predict the breed of a dog with the image of the dog as the input.