An Intelligent Fashion Object Classification Using CNN




CNN, Lenet, Fashion items, Adam, ReLu, Fashion MNIST


Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled persons. Because of visual improvement, these people face lots of issues. Hence for this group of people, there is a high need for an assistance system in terms of object recognition. For specially-abled people sometimes it becomes really difficult to identify clothing-related items from one another because of high similarity. For better object classification we use a model which includes computer vision and CNN. Computer vision is the area of AI that helps to identify visual objects. Here a CNN-based model is used for better classification of clothing and fashion items. Another model known as Lenet is used which has a stronger architectural structure. Lenet is a multi-layer convolution neural network that is mainly used for image classification tasks. For model building and validation MNIST fashion dataset is used.


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Christopher Geier, Training on test data: Removing near duplicates in Fashion-MNIST, DOI:

Han Xiao, Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, DOI:

E. Xhaferra, E. Cina and L. Toti, Classification of Standard FASHION MNIST Dataset Using Deep Learning Based CNN Algorithms, 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 2022, pp. 494-498, doi: 10.1109/ISMSIT56059.2022.9932737.

Khanday, Owais & Dadvandipour, Samad & Lone, Mohd Aaqib. (2021). Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets. IAES International Journal of Artificial Intelligence (IJ-AI). 10. 872. 10.11591/ijai.v10.i4.pp872-878.

K V, Greeshma & K., Sreekumar. (2019). Hyperparameter Optimization and Regularization on Fashion-MNIST Classification. International Journal of Recent Technology and Engineering. 8. 3713. 10.35940/ijrte.B3092.078219.

Tang, Yusi & Cui, Hanguo & Liu, Shuyong. (2020). Optimal Design of Deep Residual Network Based on Image Classification of Fashion-MNIST Dataset. Journal of Physics: Conference Series. 1624. 052011. 10.1088/1742-6596/1624/5/052011.

Kadam, Shivam & Adamuthe, Amol & Patil, Ashwini. (2020). CNN Model for Image Classification on MNIST and Fashion-MNIST Dataset. Journal of scientific research. 64. 374-384. 10.37398/JSR.2020.640251.

K V, Greeshma & K., Sreekumar. (2019). Fashion-MNIST classification based on HOG feature descriptor using SVM. International Journal of Innovative Technology and Exploring Engineering. 8. 960-962.

Vijayaraj et al, Deep Learning Image Classification for Fashion Design, Wireless Communications and Mobile Computing, 2022, DOI:

Tang, Yusi & Cui, Hanguo & Liu, Shuyong. (2020). Optimal Design of Deep Residual Network Based on Image Classification of Fashion-MNIST Dataset. Journal of Physics: Conference Series. 1624. 052011. 10.1088/1742-6596/1624/5/052011.

Zhang, K. (2018), LSTM: An Image Classification Model Based on Fashion-MNIST Dataset, Doi:

Bhatnagar, Shobhit & Ghosal, Deepanway & Kolekar, Maheshkumar. (2017). Classification of fashion article images using convolutional neural networks. 1-6. 10.1109/ICIIP.2017.8313740.

Nocentini O, Kim J, Bashir MZ, Cavallo F. Image Classification Using Multiple Convolutional Neural Networks on the Fashion-MNIST Dataset. Sensors. 2022; 22(23):9544.

Swain, Drdebabrata & Satapathy, Santosh & Acharya, Biswaranjan & Shukla, Madhu & Gerogiannis, Vassilis & Kanavos, Andreas & Giakovis, Dimitris. (2022). Deep Learning Models for Yoga Pose Monitoring. Algorithms. 15. 403. 10.3390/a15110403.

Mr. Debabrata, D. S. Kumar, and Dr Debabala, “Diagnosis of Coronary Artery Disease using 1-D Convolutional Neural Network,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2. Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, pp. 2959–2966, Jul. 30, 2019. doi: 10.35940/ijrte.b2693.078219.

Debabrata Swain et al, Improved handwritten digit recognition using artificial neural networks, International Journal of Computing Science and Mathematics, 2023 Vol.17, No.4, DOI: 10.1504/IJCSM.2023.131625

Kumar, S., Neware, N., Jain, A., Swain, D., Singh, P. (2020). Automatic Helmet Detection in Real-Time and Surveillance Video., Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore.

Chao Duana et al., Image Classification of Fashion-mnist Data Set Based on VGG Network, 2019 2nd International Conference on Information Science and Electronic Technology (ISET 2019), DOI: 10.23977/iset.2019.004

M. Y. W. Teow, Experimenting Deep Convolutional Visual Feature Learning using Compositional Subspace Representation and Fashion-MNIST, 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia, 2020, pp. 1-6, doi: 10.1109/IICAIET49801.2020.9257819.

D. Swain et al, Diabetic retinopathy using image processing and deep learning, International Journal of Computing Science and Mathematics, Vol. 14, No. 4, pp 397-409, 2022,

D. Swain et al., "A Deep Learning Framework for the Classification of Brazilian Coins," in IEEE Access, vol. 11, pp. 109448-109461, 2023, doi: 10.1109/ACCESS.2023.3321428.

Swain, D., Parmar, B., Shah, H. et al. Enhanced handwritten digit recognition using optimally selected optimizer for an ANN. Multimed Tools Appl 82, 44021–44036 (2023).

J. Shen and M. O. Shafiq, "Deep Learning Convolutional Neural Networks with Dropout - A Parallel Approach," 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, 2018, pp. 572-577, doi: 10.1109/ICMLA.2018.00092.

Hossin, Mohammad & M.N, Sulaiman. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process. 5. 01-11. 10.5121/ijdkp.2015.5201.

Lydia, Agnes & Chandrasekar, Sheela. (2022). A Comparative Study on Regularization Techniques in Convolutional Neural Networks. 784-793.

H. H. Tan and K. H. Lim, "Vanishing Gradient Mitigation with Deep Learning Neural Network Optimization," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, 2019, pp. 1-4, doi: 10.1109/ICSCC.2019.8843652.




How to Cite

Swain, D., Pandya, K., Sanghvi, J., & Manchala, Y. (2023). An Intelligent Fashion Object Classification Using CNN. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 10(4), e2.