This work was submitted as my Final project for the course Applied Artificial Intelligence at North Carolina State University . The aim of this project was to compare the efficiency of Deep Convolution Neural Networks and Recurrent ConvNets(A combination of Recurrent Neural Network and Convolution Neural Network) in Image Classification when a Multi-View Images are passed as an Input to the respective architectures.
The data was provided by the courtesy of Data Intensive Manufacturing Environment ,NCSU.The Data source is a Public CAD repository which contains CAD models of Manufacturing parts , Assemblies developed by academic researchers in the field of Advanced Manufacturing . The data used for this project was of the Categorised Parts which contained multi-view images of Manufacturing Parts(ex. bearings , bolts , Nuts , bushings etc.).
The project was completed using Google Colab.The following packages were used in the project : -scikit learn -Keras -Numpy -pandas -mpl_toolkits -matplotlib -glob