# PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

Source: [PVANet: Lightweight Deep Neural Networks for Real-time Object Detection](<https://www.sota2.com/research/paper/68fa62ff1e209131ce9fc281>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:30:18.740Z

Authors: Sanghoon Hong, Byungseok Roh, Kye-Hyeon Kim, Yeongjae Cheon, Minje Park

Published: 2016-11-23T17:43:28.000Z

## Abstract

In object detection, reducing computational cost is as important as improving accuracy for most practical usages. This paper proposes a novel network structure, which is an order of magnitude lighter than other state-of-the-art networks while maintaining the accuracy. Based on the basic principle of more layers with less channels, this new deep neural network minimizes its redundancy by adopting recent innovations including C.ReLU and Inception structure. We also show that this network can be trained efficiently to achieve solid results on well-known object detection benchmarks: 84.9% and 84.2% mAP on VOC2007 and VOC2012 while the required compute is less than 10% of the recent ResNet-101.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1611.08588>)

[PDF](<https://arxiv.org/pdf/1611.08588.pdf>)