Multiclass Classification using Enhanced MobileNet V2 Architecture

Multiclass Classification using Enhanced MobileNet V2 Architecture

Ayesha Kazi1, Prof. Sudhir Shikalpure2, Prof. Vijayshri Injamuri3

1M . Tech, Department of Computer Science and Engineering, GECA

2Assistant Prof., Department of Computer Science and Engineering, GECA

3Assistant Prof., Department of Computer Science and Engineering, GECA

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Abstract - In computer vision applications including object detection, face recognition, image classification, etc., the Convolutional Neural Network (CNN) predominates. MobileNet v2 is one such CNN architecture which can significantly cut down the parameters and cost of calculation while giving comparatively higher accuracy. In this paper, we propose the enhanced MobileNet architecture which shows an improvement in the accuracy. We have applied transfer learning techniques to fine-tune the MobileNet V2 model on the food dataset. The proposed model’s generalization capacity, it’s robustness and recognition accuracy shows a considerable amount of improvement with respect to the base architecture. The findings demonstrate that the enhanced MobileNet architecture’s accuracy for food images recognition and classification has increased to 97.16%.

Key Words: Convolutional Neural Network (CNN), MobileNet v2, transfer learning, image recognition and classification