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Performance Analysis of Image Prediction using Keras and Gradio: A Comparative Study

by Yayan Heryanto, Fauziah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 31
Year of Publication: 2023
Authors: Yayan Heryanto, Fauziah
10.5120/ijca2023923070

Yayan Heryanto, Fauziah . Performance Analysis of Image Prediction using Keras and Gradio: A Comparative Study. International Journal of Computer Applications. 185, 31 ( Aug 2023), 19-24. DOI=10.5120/ijca2023923070

@article{ 10.5120/ijca2023923070,
author = { Yayan Heryanto, Fauziah },
title = { Performance Analysis of Image Prediction using Keras and Gradio: A Comparative Study },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2023 },
volume = { 185 },
number = { 31 },
month = { Aug },
year = { 2023 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number31/32892-2023923070/ },
doi = { 10.5120/ijca2023923070 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:33.673116+05:30
%A Yayan Heryanto
%A Fauziah
%T Performance Analysis of Image Prediction using Keras and Gradio: A Comparative Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 31
%P 19-24
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research titled "Performance Analysis of Image Prediction using Keras and Gradio: A Comparative Study" aims to analyze and compare the performance of image prediction using two specific frameworks, Keras and Gradio. The study evaluates the effectiveness and efficiency of both frameworks in accurately predicting images. Testing is conducted by training and evaluating deep learning models using a dataset of images collected from 10 different animal species, with each species having 5 sample images, resulting in a total of 50 animal images tested. The research also compares various architectures and optimization techniques to enhance the predictive capabilities of the models. Performance metrics considered in the study include accuracy and training time. The results show that using Gradio for image prediction yields faster processing times compared to Keras. The average processing time using Gradio is 1.2 seconds, while with Keras, it is 3.36 seconds. Furthermore, Gradio achieves a higher accuracy rate, with 360 out of 500 (72%) correct answers, whereas Keras only reaches 345 out of 500 (69%) correct answers. These findings demonstrate that Gradio performs better in terms of accuracy and processing efficiency compared to Keras in the task of image prediction for similar animal categories. The results of this research can provide valuable insights for researchers and practitioners in selecting the most suitable framework for image prediction projects involving similar animal species.

References
  1. A. K. Gizzini, M. Chafii, A. Nimr, G. Fettweis, "Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard", IEEE 2020, pages 113751 - 113765, doi: 10.1109/ACCESS.2020.3003286, 2020.
  2. W.T. Freeman, K. Tanaka, J. Ohta, K. Kyuma, "Computer vision for computer games", IEEE 1996, doi: 10.1109/AFGR.1996.557250, 1996.
  3. S. Jiang, P. Pan, Y. Ou, C. Batten, "PyMTL3: A Python Framework for Open-Source Hardware Modeling, Generation, Simulation, and Verification", IEEE 2020, pages 58 - 66, doi: 10.1109/MM.2020.2997638, 2020.
  4. D. Grattarola, C. Alippi, "Graph Neural Networks in TensorFlow and Keras with Spektral [Application Notes]", IEEE 2021, pages 99 - 106, doi: 10.1109/MCI.2020.3039072
  5. Gradio, "Getting Started with the Gradio Python client", https://www.gradio.app/guides/getting-started-with-the-python-client, accessed on 20 July 2023.
  6. B. Kellenberger, M. Volpi, D. Tuia, "Fast animal detection in UAV images using convolutional neural networks" IEEE 2017, doi: 10.1109/IGARSS.2017.8127090, 2017.
  7. J. Gao, A. A. Proctor, Y. Shi, C. Bradley, "Hierarchical Model Predictive Image-Based Visual Servoing of Underwater Vehicles With Adaptive Neural Network Dynamic Control", IEEE 2015, pages 2323 - 2334, doi: 10.1109/TCYB.2015.2475376, 2015.
  8. K. Gu, L. Li, H. Lu, X. Min, W. Lin, "A Fast Reliable Image Quality Predictor by Fusing Micro- and Macro-Structures", IEEE 2017, pages 3903 - 3912, doi: 10.1109/TIE.2017.2652339, 2017.
  9. P. Jing, Y. Su, L. Nie, H. Gu, "Predicting Image Memorability Through Adaptive Transfer Learning From External Sources", IEEE 2016, pages 1050 - 1062, doi: 10.1109/TMM.2016.2644866, 2016.
  10. J. Choo; S. Liu, "Visual Analytics for Explainable Deep Learning", IEEE 2018, pages 84 - 92, doi: 10.1109/MCG.2018.042731661, 2018.
  11. S, R. Saufi, Z. A. B. Ahmad, M. S. Leong, M. H. Lim, "Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review, IEEE 2019, pages 122644 - 122662, doi: 10.1109/ACCESS.2019.2938227, 2019
  12. M. Pal, G. M. Foody, "Evaluation of SVM, RVM and SMLR for Accurate Image Classification With Limited Ground Data", IEEE 2012, pages 1344 - 1355, doi: 10.1109/JSTARS.2012.2215310, 2012.
  13. Z. Zhong, J. Li, Z. Luo, M. Chapman, "Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework", IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 2, doi: 10.1109/TGRS.2017.2755542, 2018.
  14. X. Lei, H. Pan, X. Huang, "A Dilated CNN Model for Image Classification", EEE Access 2019, 7, doi: 10.1109/ACCESS.2019.2927169, 2019.
  15. S. Deepak, P.M. Ameer, "Brain tumor classification using deep CNN features via transfer learning", Computers in Biology and Medicine 2019, 111, doi: 10.1016/j.compbiomed.2019.103345, 2019.
  16. W. J. Murdoch, C. Singh, K. Kumbier, "Definitions, methods, and applications in interpretable machine learning", pages 22071-220, doi: 10.1073/pnas.1900654116, 2019.
  17. G.G. Wilkinson, "Results and implications of a study of fifteen years of satellite image classification experiments", IEEE Transactions on Geoscience and Remote Sensing, 43, 3, doi: 10.1109/TGRS.2004.837325, 2005.
  18. S. Banerjee, "Animal Image Dataset (90 Different Animals)", https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals accessed on 20 July 2023.
  19. Saiharsha B., Abel Lesle A., B. Diwakar, Karthika R., Ganesan M, "Evaluating Performance of Deep Learning Architectures for Image Classification, 2020 5th International Conference on Communication and Electronics Systems (ICCES), doi: 10.1109/ICCES48766.2020.9137884, 2020.
  20. A. Shabbir, N. Ali, J. Ahmed, B. Zafar, A. Rasheed, M. Sajid, A.Ahmed, S. H. Dar,"Pattern Recognition and Deep Learning Models for Limited Labelled Data", Hindawi 2021, doi: 10.1155/2021/5843816, 2021.
  21. N. Sharma, V. Jain, A. Mishra, "An Analysis Of Convolutional Neural Networks For Image Classification", pages 377-384, doi: 10.1016/j.procs.2018.05.198, 2018.
  22. Q. Zhang, "A novel ResNet101 model based on dense dilated convolution for image classification", Published: 07 December 2021.
Index Terms

Computer Science
Information Sciences

Keywords

Image keras gradio

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