# YOLO -- You only look 10647 times

Source: [YOLO -- You only look 10647 times](<https://www.sota2.com/research/paper/68fa6ba51e209131cea2f8c9>)

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

Data updated: 2025-10-25T23:54:05.632Z

Authors: Christian Limberg, Andrew Melnik, Augustin Harter, Helge Ritter

Published: 2022-01-16T23:54:59.000Z

## Abstract

With this work we are explaining the "You Only Look Once" (YOLO) single-stage object detection approach as a parallel classification of 10647 fixed region proposals. We support this view by showing that each of YOLOs output pixel is attentive to a specific sub-region of previous layers, comparable to a local region proposal. This understanding reduces the conceptual gap between YOLO-like single-stage object detection models, RCNN-like two-stage region proposal based models, and ResNet-like image classification models. In addition, we created interactive exploration tools for a better visual understanding of the YOLO information processing streams: https://limchr.github.io/yolo\_visualization

## 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/2201.06159>)

[PDF](<https://arxiv.org/pdf/2201.06159.pdf>)