Image Analysis and Detection Using Advanced Machine Learning and Convolutional Neural Network Models

In this research, we are basically working on Convolutional Neural Networks, Machine Learning, and Image Processing for the automatic detection and classification of animal faces from an image dataset. The CNN (Convolutional Neural Network) model we made using TensorFlow was trained with a labelled dataset having both cat and dog images. Before training, we did some preprocessing, like normalisation, data augmentation and so on, so that the model can learn faster and not overfit too much. The architecture is made with a few convolutional layers and max pooling in between, after which there are some fully connected layers. This is so the network can extract different kinds of features step by step. During testing, we got high accuracy for telling between cats and dogs in both training and validation sets, so it generalises pretty well to different lighting, poses, backgrounds, etc. Then we also tested with some new images which the model had never seen before, and the results were also quite consistent and ok. Overall, this work shows that deep learning has a lot of use in animal image detection and can be used for pet monitoring, vet diagnostics or wildlife kind of applications.