[1910.01271v1] YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
These experimental results show that the proposed YOLO Nano network, created through a human-machine collaborative design strategy, provides a strong balance between accuracy, size, and computational complexity that makes it well suited for embedded object detection for edge and mobile scenarios.
Abstract: Object detection remains an active area of research in the field of computer
vision, and considerable advances and successes has been achieved in this area
through the design of deep convolutional neural networks for tackling object
detection. Despite these successes, one of the biggest challenges to widespread
deployment of such object detection networks on edge and mobile scenarios is
the high computational and memory requirements. As such, there has been growing
research interest in the design of efficient deep neural network architectures
catered for edge and mobile usage. In this study, we introduce YOLO Nano, a
highly compact deep convolutional neural network for the task of object
detection. A human-machine collaborative design strategy is leveraged to create
YOLO Nano, where principled network design prototyping, based on design
principles from the YOLO family of single-shot object detection network
architectures, is coupled with machine-driven design exploration to create a
compact network with highly customized module-level macroarchitecture and
microarchitecture designs tailored for the task of embedded object detection.
The proposed YOLO Nano possesses a model size of ~4.0MB (>15.1x and >8.3x
smaller than Tiny YOLOv2 and Tiny YOLOv3, respectively) and requires 4.57B
operations for inference (>34% and ~17% lower than Tiny YOLOv2 and Tiny YOLOv3,
respectively) while still achieving an mAP of ~69.1% on the VOC 2007 dataset
(~12% and ~10.7% higher than Tiny YOLOv2 and Tiny YOLOv3, respectively).
Experiments on inference speed and power efficiency on a Jetson AGX Xavier
embedded module at different power budgets further demonstrate the efficacy of
YOLO Nano for embedded scenarios.
‹Figure 1: YOLO Nano network architecture. Note that PEP(x) indicates x channels in the first projection layer of a residual PEP module, and FCA(x) indicates reduction ratio of x (YOLO Nano Architectural Design)›