# Creating Lightweight Object Detectors with Model Compression for Deployment on Edge Devices

Source: [Creating Lightweight Object Detectors with Model Compression for Deployment on Edge Devices](<https://www.sota2.com/research/paper/68fa65bf1e209131cea0c257>)

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

Data updated: 2025-10-25T21:34:37.515Z

Authors: Yiwu Yao, Weiqiang Yang, Haoqi Zhu

Published: 2019-05-06T01:52:17.000Z

## Abstract

To achieve lightweight object detectors for deployment on the edge devices, an effective model compression pipeline is proposed in this paper. The compression pipeline consists of automatic channel pruning for the backbone, fixed channel deletion for the branch layers and knowledge distillation for the guidance learning. As results, the Resnet50-v1d is auto-pruned and fine-tuned on ImageNet to attain a compact base model as the backbone of object detector. Then, lightweight object detectors are implemented with proposed compression pipeline. For instance, the SSD-300 with model size=16.3MB, FLOPS=2.31G, and mAP=71.2 is created, revealing a better result than SSD-300-MobileNet.

## 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/1905.01787>)

[PDF](<https://arxiv.org/pdf/1905.01787.pdf>)