Differentiating Dogs and Cats; Using advanced Deep Learning Techniques for Image Recognition

This work proposes to classify images of dogs and cats using state-of- modern machine learning techniques. Developing a model of precisely identifying between dog and cat images using CNN is the objective.

This work tries to use deep learning methods to classify images of dogs and cats. The objective is to use CNN model to develop a system capable of differentiating images of dogs from cats with efficiency.

Methodology:

1. Data Collection: Observing Kaggle Dogs Cats dataset, which compare a vast number of categorised images of both dogs and cats.
2. Data Pre-processing – This step involves transformation of image data from original form to enhance performance and resistance by augmentation, normalisation
3. Train the Models: Use CNN models VGG16 and ResNet to classify images.
4. Training Model – Training the Convolution Neural Network (CNN) models using Dataset, and adjusting Hyperparameters to get maximum
5. Model Evaluation: The performance of a model can be assessed by using metrics such as accuracy, precision, recall and F1-score.

These results show that the ResNet model achieved highest accuracy for classifying dog and cat images. In addition to the relevance in terms of facial recognition tasks, this paper demonstrates how efficient deep learning methods can be for the image classification process and provides key learnings on model selection & training.