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
1
arXiv:1506.02640v5 [cs.CV] 9 May 2016
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