YOLOv3: An Incremental Improvement
Joseph Redmon Ali Farhadi
University of Washington
Abstract
We present some updates to YOLO! We made a bunch
of little design changes to make it better. We also trained
this new network that’s pretty swell. It’s a little bigger than
last time but more accurate. It’s still fast though, don’t
worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP,
as accurate as SSD but three times faster. When we look
at the old .5 IOU mAP detection metric YOLOv3 is quite
good. It achieves 57.9 AP
50
in 51 ms on a Titan X, com-
pared to 57.5 AP
50
in 198 ms by RetinaNet, similar perfor-
mance but 3.8× faster. As always, all the code is online at
https://pjreddie.com/yolo/.
1. Introduction
Sometimes you just kinda phone it in for a year, you
know? I didn’t do a whole lot of research this year. Spent
a lot of time on Twitter. Played around with GANs a little.
I had a little momentum left over from last year [12] [1]; I
managed to make some improvements to YOLO. But, hon-
estly, nothing like super interesting, just a bunch of small
changes that make it better. I also helped out with other
people’s research a little.
Actually, that’s what brings us here today. We have
a camera-ready deadline [4] and we need to cite some of
the random updates I made to YOLO but we don’t have a
source. So get ready for a TECH REPORT!
The great thing about tech reports is that they don’t need
intros, y’all know why we’re here. So the end of this intro-
duction will signpost for the rest of the paper. First we’ll tell
you what the deal is with YOLOv3. Then we’ll tell you how
we do. We’ll also tell you about some things we tried that
didn’t work. Finally we’ll contemplate what this all means.
2. The Deal
So here’s the deal with YOLOv3: We mostly took good
ideas from other people. We also trained a new classifier
network that’s better than the other ones. We’ll just take
you through the whole system from scratch so you can un-
derstand it all.
50 100 150 200 250
inference time (ms)
28
30
32
34
36
38
COCO AP
B C
D
E
F
G
RetinaNet-50
RetinaNet-101
YOLOv3
Method
[B] SSD321
[C] DSSD321
[D] R-FCN
[E] SSD513
[F] DSSD513
[G] FPN FRCN
RetinaNet-50-500
RetinaNet-101-500
RetinaNet-101-800
YOLOv3-320
YOLOv3-416
YOLOv3-608
mAP
28.0
28.0
29.9
31.2
33.2
36.2
32.5
34.4
37.8
28.2
31.0
33.0
time
61
85
85
125
156
172
73
90
198
22
29
51
Figure 1. We adapt this figure from the Focal Loss paper [9].
YOLOv3 runs significantly faster than other detection methods
with comparable performance. Times from either an M40 or Titan
X, they are basically the same GPU.
2.1. Bounding Box Prediction
Following YOLO9000 our system predicts bounding
boxes using dimension clusters as anchor boxes [15]. The
network predicts 4 coordinates for each bounding box, t
x
,
t
y
, t
w
, t
h
. If the cell is offset from the top left corner of the
image by (c
x
, c
y
) and the bounding box prior has width and
height p
w
, p
h
, then the predictions correspond to:
b
x
= σ(t
x
) + c
x
b
y
= σ(t
y
) + c
y
b
w
= p
w
e
t
w
b
h
= p
h
e
t
h
During training we use sum of squared error loss. If the
ground truth for some coordinate prediction is
ˆ
t
*
our gra-
dient is the ground truth value (computed from the ground
truth box) minus our prediction:
ˆ
t
*
− t
*
. This ground truth
value can be easily computed by inverting the equations
above.
YOLOv3 predicts an objectness score for each bounding
box using logistic regression. This should be 1 if the bound-
ing box prior overlaps a ground truth object by more than
any other bounding box prior. If the bounding box prior
1
arXiv:1804.02767v1 [cs.CV] 8 Apr 2018
