Convolution Neural Network Based Image Classifier
Hussain Ejaz,Syed Mehdi +1 more
- 11 Mar 2021
TL;DR: The objective of the project propounded in the paper, is applying the abstract of an algorithm of Deep Learning, viz, Convolutional neural networks (CNN) for multiple image classification using VGG19 model of CNN on variegated datasets.
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Abstract: The concept of Deep Learning is emanated in machine learning as an enhanced research area and is empirical to various image applications. The objective of the project propounded in the paper, is applying the abstract of an algorithm of Deep Learning, viz, Convolutional neural networks (CNN) for multiple image classification. The algorithm is assessed on variegated datasets, which consist 2399 images taken from google, myntra fashion clothes, etc. The algorithm’s performance is gauged based on the quality metric known as Confusion Matrix. The analysis is done and the model successfully classifies each image using VGG19 model of CNN.
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References
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
- 01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
53.5K
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 01 Jan 2015
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
51.9K
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner,Patrick Haffner +7 more
- 01 Jan 2001
TL;DR: This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task, and Convolutional neural networks are shown to outperform all other techniques.
32.7K
Receptive fields and functional architecture of monkey striate cortex
David H. Hubel,Torsten N. Wiesel +1 more
TL;DR: The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light, with response properties very similar to those previously described in the cat.
7K