[Important] This git only keeps some selected best results of this project, while most experiments were conducted on HiperGator. Therefore the contribution ratio shown here is not accurate.
Abstract: Analyzing and classifying pictures is quite a heating topic in nowadays computers vision field, but may cost great computational resource. Cloud computing enables users and enterprises from different fields to share the same resource to process big data and big algorithm. The Convolutional Neural Network (CNN) has been a dominant approach to do image classification since 2011, while DenseNet CNN is the most recent state-of-the-art structure. We proposed three variations upon DenseNet, and successfully reduced the computational cost but retained performance. We cut off low efficient connections between layers, then conducted several experiments to discuss connection features, multiform blocks, growth, learning curves, and etc. Finally, we built a dynamic website with the best pretrained network to do image classification. All computations benefit from cloud computing technology.
Doc folder contains proposal, midterm, and final papers and slides of this project.
Experiments folder contains part of the experiments we have done related to the final result.
Referred Git folder contains some tools that we have used from other gits.
RS folder contains the reading summaries we wrote.
Website folder contains the demo website we make on the image classifiation.
It is written in Python.