Back to articles
Articles
Volume: 32 | Article ID: art00003
Image
A Visualization System for Performance Analysis of Image Classification Models
  DOI :  10.2352/ISSN.2470-1173.2020.1.VDA-375  Published OnlineJanuary 2020
Abstract

Developing machine learning models for image classification problems involves various tasks such as model selection, layer design, and hyperparameter tuning for improving the model performance. However, regarding deep learning models, insufficient model interpretability renders it infeasible to understand how they make predictions. To facilitate model interpretation, performance analysis at the class and instance levels with model visualization is essential. We herein present an interactive visual analytics system to provide a wide range of performance evaluations of different machine learning models for image classification. The proposed system aims to overcome challenges by providing visual performance analysis at different levels and visualizing misclassification instances. The system which comprises five views - ranking, projection, matrix, and instance list views, enables the comparison and analysis different models through user interaction. Several use cases of the proposed system are described and the application of the system based on MNIST data is explained. Our demo app is available at https://chanhee13p.github.io/VisMlic/.

Subject Areas :
Views 16
Downloads 1
 articleview.views 16
 articleview.downloads 1
  Cite this article 

Chanhee Park, Hyojin Kim, Kyungwon Lee, "A Visualization System for Performance Analysis of Image Classification Modelsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visualization and Data Analysis,  2020,  pp 375-1 - 375-9,  https://doi.org/10.2352/ISSN.2470-1173.2020.1.VDA-375

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2020
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA