What question did this study set out to answer?

The aim is to evaluate the performance of a simple convolutional neural network in image classification tasks.

March 1, 2017411 citations

Simple convolutional neural network on image classification

Key Points

The aim is to evaluate the performance of a simple convolutional neural network in image classification tasks.
Built and tested a simple convolutional neural network for image classification.
Used benchmarking datasets like MNIST and CIFAR-10 for evaluation.
Analyzed the effects of learning rate settings and optimization algorithms on classification performance.
Demonstrated high performance in image classification using a simple convolutional neural network.
Found optimal learning rates and algorithms significantly enhance classification accuracy.

Abstract

In recent years, deep learning has been used in image classification, object tracking, pose estimation, text detection and recognition, visual saliency detection, action recognition and scene labeling. Auto Encoder, sparse coding, Restricted Boltzmann Machine, Deep Belief Networks and Convolutional neural networks is commonly used models in deep learning. Among different type of models, Convolutional neural networks has been demonstrated high performance on image classification. In this paper we bulided a simple Convolutional neural network on image classification. This simple Convolutional neural network completed the image classification. Our experiments are based on benchmarking datasets minist 1 and cifar-10. On the basis of the Convolutional neural network, we also analyzed different methods of learning rate set and different optimization algorithm of solving the optimal parameters of the influence on image classification.

问 AI

Bookmark

View Full Paper