| Summary, etc |
Classification and identification of images from images from objects using machine learning.<br/>In our world today, object classification and identification helps detect and define objects such<br/>as humans, buildings and cars from digital images, it is a computer technology related computer<br/>vision and image processing. The aim was to identify and classify objects using convolutional<br/>neural network and the objectives; to formulate an algorithm, develop a model and evaluate<br/>the model. This project work presents the building process of a model to identify and classify<br/>object images from different objects. This chapter reports the design phase of the model, it<br/>involves decomposing the whole system into smaller parts and defining the relationship among<br/>the constituent parts.<br/>Top down design approach was employed in design and development phases of this project<br/>work. This involved dividing the system into subsystems or modules and each subsystem being<br/>further divided into even smaller subs. This process of division is repeated until each module<br/>is sufficiently small enough to be conveniently coded (implemented) from scratch as an<br/>independent entity that performs a clearly defined operation. At the beginning of the project,<br/>the tools to be used for this project were downloaded, the tools used for the project were the<br/>Kaggle Dataset API, Google Chrome, A Kaggle JSON file.<br/>One of the libraries used was matplotlib. Matplotlib is a Python 2D plotting library which<br/>produces publication quality figures in a variety of hardcopy formats and interactive<br/>environments across platforms. Matplotlib can be used in Python scripts, the Python<br/>and IPython shells, the Jupyter notebook, web application servers, and four graphical user<br/>interface toolkits.<br/>Classification and identification of images from images from objects using machine learning.<br/>In out world today,object classification and identification helps detect and define objects such<br/>as humans, buildings and cars from digital images, it is a computer technology related computer<br/>vision and image processing. The aim was to identify and classify objects using convolutional<br/>neural network and the objectives;to formulate an algorithm,develop a model and evaluate the<br/>model. This project work presents the building process of a model to identify and classify<br/>object images from different objects. This chapter reports the design phase of the model, it<br/>involves decomposing the whole system into smaller parts and defining the relationship among<br/>the constituent parts.<br/>vi<br/>Top down design approach was employed in design and development phases of this project<br/>work. This involved dividing the system into subsystems or modules and each subsystem being<br/>further divided into even smaller subs. This process of division is repeated until each module<br/>is sufficiently small enough to be conveniently coded (implemented) from scratch as an<br/>independent entity that performs a clearly defined operation. At the beginning of the project,<br/>the tools to be used for this project were downloaded, the tools used for the project were the<br/>Kaggle Dataset API, Google Chrome, A Kaggle JSON file.<br/>One of the libraries used was matplotlib. Matplotlib is a Python 2D plotting library which<br/>produces publication quality figures in a variety of hardcopy formats and interactive<br/>environments across platforms. Matplotlib can be used in Python scripts, the Python<br/>and IPython shells, the Jupyter notebook, web application servers, and four graphical user<br/>interface toolkits.<br/>All this methods and materials were deployed for this project and object classification and<br/>identification such as humans,cars,buildings was successful. |
