Performance Analysis of Deep Neural Network based on Transfer Learning for Pet Classification

Abstract

Deep learning frameworks have progressed beyond human recognition capabilities and, now it’s the perfect opportunity to optimize them for implementation on the embedded platforms. The present deep learning architectures support learning capabilities, but they lack flexibility for applying learned knowledge on the tasks in other unfamiliar domains. This work tries to fill this gap with the deep neural network-based solution for object detection in unrelated domains with a focus on the reduced footprint of the developed model. Knowledge distillation provides efficient and effective teacher-student learning for a variety of different visual recognition tasks. A lightweight student network can be easily trained under the guidance of the high-capacity teacher networks. The teacher-student architecture implementation on binary classes shows a 20% improvement in accuracy within the same training iterations using the transfer learning approach. The scalability of the student model is tested with binary, ternary and multiclass and their performance is compared on basis of inference speed. The results show that the inference speed does not depend on the number of classes. For similar recognition accuracy, the inference speed of about 50 frames per second or 20ms per image. Thus, this approach can be generalized as per the application requirement with minimal changes, provided the dataset format compatibility.