TY - GEN
T1 - Real-Time Applicability Analysis of Lightweight Models on Jetson Nano Using TensorFlow-Lite
AU - Vidya, Kamath
AU - Renuka, A.
AU - Vanajakshi, J.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Deep learning models have recently acquired prominence due to their adaptability to constrained devices. Because of this possibility, a significant number of studies in the fields of IoT and Robotics are being done with the goal of deploying deep learning models on resource-constrained applications. A variety of lightweight models are now available that can perform computer vision tasks on constrained devices including the Jetson Nano. However, several enhancements are still needed if this field of research has to prosper in the future. This study was carried out with the aim of comparing and contrasting the lightweight models provided by TensorFlow in order to assess them and ascertain how close they are to practical reality. The conclusions not only present the observed outcomes but also provide insight into the models, attempting to identify potential improvements.
AB - Deep learning models have recently acquired prominence due to their adaptability to constrained devices. Because of this possibility, a significant number of studies in the fields of IoT and Robotics are being done with the goal of deploying deep learning models on resource-constrained applications. A variety of lightweight models are now available that can perform computer vision tasks on constrained devices including the Jetson Nano. However, several enhancements are still needed if this field of research has to prosper in the future. This study was carried out with the aim of comparing and contrasting the lightweight models provided by TensorFlow in order to assess them and ascertain how close they are to practical reality. The conclusions not only present the observed outcomes but also provide insight into the models, attempting to identify potential improvements.
UR - http://www.scopus.com/inward/record.url?scp=85187802920&partnerID=8YFLogxK
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U2 - 10.1007/978-981-99-8129-8_24
DO - 10.1007/978-981-99-8129-8_24
M3 - Conference contribution
AN - SCOPUS:85187802920
SN - 9789819981281
T3 - Lecture Notes in Networks and Systems
SP - 285
EP - 294
BT - Machine Intelligence for Research and Innovations - Proceedings of MAiTRI 2023
A2 - Verma, Om Prakash
A2 - Wang, Lipo
A2 - Kumar, Rajesh
A2 - Yadav, Anupam
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023
Y2 - 1 September 2023 through 3 September 2023
ER -