Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
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Construction of Efficient Smart Voting Machine with Liveness Detection Module
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An Economical Robotic Arm for Playing Chess Using Visual Servoing
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Triplet loss for Chromosome Classification
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Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
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Real Time Sign Language Recognition and Speech Generation
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Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 4 | Issue - 4 | december 2022
Published
25 January, 2023
Image classification is a part of computer vision, in which the digital system categorizes the entire image. Deep Learning (DL) models are widely used for image classification. However, creating DL models is resource-intensive and time-consuming, and requires extensive knowledge in the DL domain. Google Teachable Machines (GTM) is a website that outputs a trained model given the dataset, however, GTM uses only the MobileNet model and does not balance the image dataset which affects the model’s accuracy. This paper proposes a tool that automates the steps in building and training an image classification model. Using this tool does not require any extensive knowledge in DL. The tool automates the image data pre-processing steps, model building, model training, and model testing to output the best model for the given image classification dataset based on the test accuracy. The tool is tested on two datasets (each balanced and unbalanced dataset): a custom construction dataset and a Minet dataset. Both datasets are also used to train models using the GTM website. Due to the automated pre-processing steps, the average increase in the accuracy is 14.55% in the construction dataset and 3.91% in the Minet dataset. Comparing to the GTM models, the tool produced model with 8.33% more accuracy on the construction dataset and model with 14.07% more accuracy on the Minet dataset. The models trained by the proposed tool have better accuracy compared to the models obtained using GTM. Thus, using the image classification model selector facilitates the creation of an effective image classification model for the target dataset.
KeywordsImage classification automated image classification model selection automated image classification
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