Image classification is a widely discussed topic in this era. It covers a vivid range of application domains like from garbage classification applications to advanced fields of medical sciences. There have been several research works that have been done in the past and are also currently under research for coming up with better-optimized image classification techniques. However, the process of image classification turns out to be time-consuming. This work deals with the widely accepted FashionMNIST (modified national institute of standards and technology database) dataset, having a set of sixty thousand images for training a model and another popular dataset of MNIST for handwritten numbers. The work compares several convolutional neural network (CNN) models and aims in parallelizing them using a distributed framework that is provided by the python library, RAY. The parallelization has been achieved over the multiple cores of CPU and many cores of GPU. The work also shows that the overall accuracy of the system is not affected by the parallelization.
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Image Classification Using CNN With Multi-Core and Many-Core Architecture
Semantic Scholar · Computer Science · 2021
Abstract
Image classification is a widely discussed topic in this era. It covers a vivid range of application domains like from garbage classification applications to advanced fields of medical sciences. There have been several research works that have been done in the past and are also currently under research for coming up with better-optimized image classification techniques. However, the process of image classification turns out to be time-consuming. This work deals with the widely accepted FashionMNIST (modified national institute of standards and technology database) dataset, having a set of sixty thousand images for training a model and another popular dataset of MNIST for handwritten numbers. The work compares several convolutional neural network (CNN) models and aims in parallelizing them using a distributed framework that is provided by the python library, RAY. The parallelization has been achieved over the multiple cores of CPU and many cores of GPU. The work also shows that the overall accuracy of the system is not affected by the parallelization.