# YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers

Source: [YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers](<https://www.sota2.com/research/paper/68fa65181e209131cea0842c>)

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

Data updated: 2025-10-27T05:56:48.736Z

Authors: Jonathan Pedoeem, Rachel Huang

Published: 2018-11-14T01:20:08.000Z

## Abstract

This paper focuses on YOLO-LITE, a real-time object detection model developed to run on portable devices such as a laptop or cellphone lacking a Graphics Processing Unit (GPU). The model was first trained on the PASCAL VOC dataset then on the COCO dataset, achieving a mAP of 33.81% and 12.26% respectively. YOLO-LITE runs at about 21 FPS on a non-GPU computer and 10 FPS after implemented onto a website with only 7 layers and 482 million FLOPS. This speed is 3.8x faster than the fastest state of art model, SSD MobilenetvI. Based on the original object detection algorithm YOLOV2, YOLO- LITE was designed to create a smaller, faster, and more efficient model increasing the accessibility of real-time object detection to a variety of devices.

## 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/1811.05588>)

[PDF](<https://arxiv.org/pdf/1811.05588.pdf>)