Classify Cat and Dog Images with VGG-16

Picture Source: Newsweek

Context

Project was conducted as part of the Computer Vision lecture with the code 02010012, which is a course offered by the Computer Engineering faculty. Computer Vision is an interdisciplinary field of study that focuses on enabling computers to interpret and understand visual information from the world around us. It involves the use of various techniques, including image processing, pattern recognition, and machine learning, to analyze and interpret digital images and videos. This project specifically focused on the use of deep learning, which is a subfield of machine learning that involves the use of neural networks to model complex relationships between input and output data. The VGG-16 model is a deep neural network architecture that has proven to be highly effective in image classification tasks, as demonstrated by its success in the ImageNet Large Scale Visual Recognition Challenge.

By applying this knowledge and utilizing the VGG-16 model, you were able to develop a high-performing image classification model for cats and dogs. This project showcases the practical application of computer vision and deep learning techniques in solving real-world problems, and highlights the importance of these techniques in the field of computer engineering.

The objective of this project is to develop a deep learning model using the VGG-16 architecture to classify images of cats and dogs with high accuracy. The model will be trained on a dataset of labeled images and evaluated. The ultimate goal is to create a model that can accurately distinguish between images of cats and dogs in real-world scenarios.

The dataset consists of 1,425 digital images of domestic animals, specifically 713 images of dogs and 712 images of cats. These images were collected for the purpose of training a deep learning model to classify images of cats and dogs with high accuracy. All images were obtained from various online sources and manually labeled by experts to ensure accuracy of the dataset. The images vary in size and resolution, and were preprocessed to remove any artifacts or unwanted information.

Objectives

The objective of this project is to develop a deep learning model using the VGG-16 architecture to classify images of cats and dogs with high accuracy. The model will be trained on a dataset of labeled images and evaluated. The ultimate goal is to create a model that can accurately distinguish between images of cats and dogs in real-world scenarios.

• Understand the dataset and batches.
• Build classification models to predict the class.
• Evaluate the model.
• Upload and predict your own picture.

Keywords

• Computer Science
• Classification
• Convolutional Neural Networks (CNN, or ConvNet)
• Cat & Dog Classification

Notebooks

On below, there are informations about the notebooks created respectively.

1. In this section, I build classification model_1 with VGG-16. After that, predictions and evaluations (plot train - validation loss and accuracy graph) made from train and validation data.
   Notebook:

   1. cat_dog_vgg16_1.ipynb

2. In this section, I build classification model_2 with VGG-16. After that, predictions and evaluations (train-validation loss and accuracy graph, F1 score, precision, recall, support, ROC graph and AUC score) made from train, validation and test data. The results of the model on the test data are in the model_predictions.csv file. Just click the link to view.
   Notebook:

   1. cat_dog_vgg16_2.ipynb

License

GNU General Public License v3.0

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