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You Only Look Once:

Uniﬁed, Real-Time Object Detection

Joseph Redmon

∗

, Santosh Divvala

∗†

, Ross Girshick

, Ali Farhadi

∗†

University of Washington

∗

, Allen Institute for AI

†

, Facebook AI Research

http://pjreddie.com/yolo/

Abstract

We present YOLO, a new approach to object detection.

Prior work on object detection repurposes classiﬁers to per-

form detection. Instead, we frame object detection as a re-

gression problem to spatially separated bounding boxes and

associated class probabilities. A single neural network pre-

dicts bounding boxes and class probabilities directly from

full images in one evaluation. Since the whole detection

pipeline is a single network, it can be optimized end-to-end

directly on detection performance.

Our uniﬁed architecture is extremely fast. Our base

YOLO model processes images in real-time at 45 frames

per second. A smaller version of the network, Fast YOLO,

processes an astounding 155 frames per second while

still achieving double the mAP of other real-time detec-

tors. Compared to state-of-the-art detection systems, YOLO

makes more localization errors but is less likely to predict

false positives on background. Finally, YOLO learns very

general representations of objects. It outperforms other de-

tection methods, including DPM and R-CNN, when gener-

alizing from natural images to other domains like artwork.

1. Introduction

Humans glance at an image and instantly know what ob-

jects are in the image, where they are, and how they inter-

act. The human visual system is fast and accurate, allow-

ing us to perform complex tasks like driving with little con-

scious thought. Fast, accurate algorithms for object detec-

tion would allow computers to drive cars without special-

ized sensors, enable assistive devices to convey real-time

scene information to human users, and unlock the potential

for general purpose, responsive robotic systems.

Current detection systems repurpose classiﬁers to per-

form detection. To detect an object, these systems take a

classiﬁer for that object and evaluate it at various locations

and scales in a test image. Systems like deformable parts

models (DPM) use a sliding window approach where the

classiﬁer is run at evenly spaced locations over the entire

image [

10].

More recent approaches like R-CNN use region proposal

1. Resize image.

2. Run convolutional network.

3. Non-max suppression.

Dog: 0.30

Person: 0.64

Horse: 0.28

Figure 1: The YOLO Detection System. Processing images

with YOLO is simple and straightforward. Our system (1) resizes

the input image to 448 × 448, (2) runs a single convolutional net-

work on the image, and (3) thresholds the resulting detections by

the model’s conﬁdence.

methods to ﬁrst generate potential bounding boxes in an im-

age and then run a classiﬁer on these proposed boxes. After

classiﬁcation, post-processing is used to reﬁne the bound-

ing boxes, eliminate duplicate detections, and rescore the

boxes based on other objects in the scene [

13]. These com-

plex pipelines are slow and hard to optimize because each

individual component must be trained separately.

We reframe object detection as a single regression prob-

lem, straight from image pixels to bounding box coordi-

nates and class probabilities. Using our system, you only

look once (YOLO) at an image to predict what objects are

present and where they are.

YOLO is refreshingly simple: see Figure

1. A sin-

gle convolutional network simultaneously predicts multi-

ple bounding boxes and class probabilities for those boxes.

YOLO trains on full images and directly optimizes detec-

tion performance. This uniﬁed model has several beneﬁts

over traditional methods of object detection.

First, YOLO is extremely fast. Since we frame detection

as a regression problem we don’t need a complex pipeline.

We simply run our neural network on a new image at test

time to predict detections. Our base network runs at 45

frames per second with no batch processing on a Titan X

GPU and a fast version runs at more than 150 fps. This

means we can process streaming video in real-time with

less than 25 milliseconds of latency. Furthermore, YOLO

achieves more than twice the mean average precision of

other real-time systems. For a demo of our system running

in real-time on a webcam please see our project webpage:

http://pjreddie.com/yolo/.

Second, YOLO reasons globally about the image when

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