Multi-Class Image Classification using CNN and Tflite

Authors

  • Vishal Shah Student, Department of Computer Engineering, Chandubhai S. Patel Institute of Science & Technology, Anand, India
  • Neha Sajnani Student, Department of Computer Engineering, Chandubhai S. Patel Institute of Science & Technology, Anand, India

DOI:

https://doi.org/10.47607/ijresm.2020.375

Keywords:

Android, Convolutional Neural Network, Deep learning, Image classification, Tflite

Abstract

In recent years’ machine learning is playing a vital role in our everyday lifelike, it can help us to route somewhere, find something for what we aren’t aware of, or can schedule appointments in seconds. Looking at the other side of the coin besides machine learning Mobile phones are equivocating and competing in the same field. If we take an optimistic view, by applying machine learning in our mobile devices, we can make our lives better and even move society forward. Image Classification is the most common and trending topic of machine learning. Among several different types of models in deep learning, Convolutional Neural Networks (CNN’s) have intimated high performance on image classification which are made out of various handling layers to gain proficiency with the portrayals of information with numerous unique levels, are the best AI models as of late. Here, we have trained a simple CNN and completed the experiments on the dataset called Fashion Mnist and Flower Recognition, and also analyzed the techniques of integrating the trained model in the Android platform.

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Published

2020-11-20

How to Cite

[1]
V. . Shah and N. . Sajnani, “Multi-Class Image Classification using CNN and Tflite”, International Journal of Research in Engineering, Science and Management, vol. 3, no. 11, pp. 65–68, Nov. 2020, doi: 10.47607/ijresm.2020.375.

Issue

Section

Articles