Implementation and Evaluation of MobileNetV2 for Binary Image Classification on the Cats vs Dogs Dataset Using Transfer Learning
Kata Kunci:
MobileNetV2, transfer learning, binary image classification, lightweight CNN, computer visionAbstrak
This study presents the implementation and evaluation of a lightweight deep learning model based on MobileNetV2 for binary image classification of cats and dogs. Utilizing transfer learning with pretrained ImageNet weights, the model was fine-tuned on a balanced dataset of 24,998 labeled images sourced from Kaggle. The training pipeline incorporated data preprocessing and augmentation techniques, followed by two-phase training: feature extraction and fine-tuning. The final model achieved 98.9% validation accuracy, with precision, recall, and F1-score each reaching 0.99. The architecture, with only ~3.5 million trainable parameters and a file size of 14 MB, demonstrated fast inference (≈50 ms/image on GPU) and strong generalization. Despite its high performance, the model exhibited limitations under poor lighting, partial occlusion, and grayscale inputs. These findings confirm that MobileNetV2, when properly fine-tuned, offers an effective and efficient solution for real-time binary image classification tasks, and holds promise for future deployment in edge and mobile environments
Unduhan
Referensi
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. (2020). GhostNet: More features from cheap operations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1580–1589). https://doi.org/10.1109/CVPR42600.2020.00165
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., ... & Adam, H. (2019). Searching for MobileNetV3. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1314–1324). https://doi.org/10.1109/ICCV.2019.00140
Kanavos, A., Vonitsanos, G., Maragoudakis, M., & Mylonas, P. (2025). Designing and regularizing deep CNN architectures for dog versus cat image classification. In Proceedings of the International Conference on Novel and Intelligent Digital Systems (NiDS 2025) (pp. 309–320). Springer. https://doi.org/10.1007/978-3-032-06634-3_25
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NeurIPS) (pp. 1097–1105). https://doi.org/10.1145/3065386
Lee, J., & Kim, M. (2024). Rare data image classification system using few-shot learning. Electronics, 13(19), 3923. https://doi.org/10.3390/electronics13193923
Nirupama, & Virupakshappa. (2024). MobileNet-V2: An enhanced skin disease classification by attention and multi-scale features. Journal of Imaging Informatics in Medicine. https://doi.org/10.1007/s10278-024-01271-y
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4510–4520). https://doi.org/10.1109/CVPR.2018.00474
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60. https://doi.org/10.1186/s40537-019-0197-0
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (ICML) (pp. 6105–6114). https://arxiv.org/abs/1905.11946
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019). MnasNet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2820–2828). https://doi.org/10.1109/CVPR.2019.00291
Trigka, M., & Dritsas, E. (2025). A comprehensive survey of deep learning approaches in image processing. Sensors, 25(2), 531. https://doi.org/10.3390/s25020531
Wong, K., Gatt, A., Stamatescu, V., & McDonnell, M. D. (2016). Understanding data augmentation for classification: When to warp? In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA). https://doi.org/10.1109/DICTA.2016.7796996
Yong, L., Ma, L., Sun, D., & Du, L. (2023). Application of MobileNetV2 to waste classification. PLOS ONE, 18(3), e0282336. https://doi.org/10.1371/journal.pone.0282336
Yu, F., Xiu, X., & Li, Y. (2022). A survey on deep transfer learning and beyond. Mathematics, 10(19), 3619. https://doi.org/10.3390/math10193619
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6848–6856). https://doi.org/10.1109/CVPR.2018.00716
Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8697–8710). https://doi.org/10.1109/CVPR.2018.00907
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2025 walif (Author)
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
