YOLOv3: An Incremental Improvement
Joseph Redmon, Ali Farhadi
University of Washington

38

AbstractG

36

We present some updates to YOLO! We made a bunchof little design changes to make it better. We also trained
this new network that’s pretty swell. It’s a little bigger thanlast 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 lookat the old .5 IOU mAP detection metric YOLOv3 is quite
good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared to 57.5 AP50 in 198 ms by RetinaNet, similar performance but 3.8× faster. As always, all the code is online athttps://pjreddie.com/yolo/.

COCO AP

34

F

32

E
D

30
28
50

B

C
100

YOLOv3RetinaNet-50
RetinaNet-101

mAP
Method28.0[B] SSD321
28.0[C] DSSD321
29.9[D] R-FCN[E] SSD513
31.2[F] DSSD513
33.2
36.2
[G] FPN FRCN
RetinaNet-50-500
32.5
RetinaNet-101-500 34.4
RetinaNet-101-800 37.8
YOLOv3-320
28.2
YOLOv3-416
31.0
33.0
YOLOv3-608

150

200

inference time (ms)

time
61
85
85
125
156
172
73
90
198
22
29
51

250

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.

1. Introduction

2.1. Bounding Box Prediction

Sometimes you just kinda phone it in for a year, you
know? I didn’t do a whole lot of research this year. Spenta 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, honestly, nothing like super interesting, just a bunch of smallchanges that make it better. I also helped out with otherpeople’s research a little.
Actually, that’s what brings us here today. We havea camera-ready deadline [4] and we need to cite some of
the random updates I made to YOLO but we don’t have asource. So get ready for a TECH REPORT!
The great thing about tech reports is that they don’t needintros, y’all know why we’re here. So the end of this introduction 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.

Following YOLO9000 our system predicts boundingboxes using dimension clusters as anchor boxes [15]. The
network predicts 4 coordinates for each bounding box, tx ,ty , tw , th . If the cell is offset from the top left corner of theimage by (cx , cy ) and the bounding box prior has width andheight pw , ph , then the predictions correspond to:bx = σ(tx ) + cx
by = σ(ty ) + cy
bw = pw etw
bh = ph eth

During training we use sum of squared error loss. If theground truth for some coordinate prediction is t̂* our gradient is the ground truth value (computed from the groundtruth box) minus our prediction: t̂* − t* . This ground truth
value can be easily computed by inverting the equations
above.
YOLOv3 predicts an objectness score for each boundingbox using logistic regression. This should be 1 if the bounding box prior overlaps a ground truth object by more thanany other bounding box prior. If the bounding box prior

2. The Deal
So here’s the deal with YOLOv3: We mostly took goodideas from other people. We also trained a new classifier
network that’s better than the other ones. We’ll just takeyou through the whole system from scratch so you can understand it all.
1

cx
pw

cy

bw
ph

bh

σ(ty)
σ(tx)

bx=σ(tx)+cx
by=σ(ty)+cy
bw=pwetbh=phetwhFigure 2. Bounding boxes with dimension priors and location
prediction. We predict the width and height of the box as offsetsfrom cluster centroids. We predict the center coordinates of thebox relative to the location of filter application using a sigmoid
function. This figure blatantly self-plagiarized from [15].

Next we take the feature map from 2 layers previous andupsample it by 2×. We also take a feature map from earlier
in the network and merge it with our upsampled featuresusing concatenation. This method allows us to get moremeaningful semantic information from the upsampled features and finer-grained information from the earlier feature
map. We then add a few more convolutional layers to process this combined feature map, and eventually predict a
similar tensor, although now twice the size.
We perform the same design one more time to predictboxes for the final scale. Thus our predictions for the 3rd
scale benefit from all the prior computation as well as finegrained features from early on in the network.
We still use k-means clustering to determine our bounding box priors. We just sort of chose 9 clusters and 3
scales arbitrarily and then divide up the clusters evenly
across scales. On the COCO dataset the 9 clusters were:(10 × 13), (16 × 30), (33 × 23), (30 × 61), (62 × 45), (59 ×119), (116 × 90), (156 × 198), (373 × 326).

2.4. Feature Extractor
is not the best but does overlap a ground truth object bymore than some threshold we ignore the prediction, following [17]. We use the threshold of .5. Unlike [17] our system
only assigns one bounding box prior for each ground truth
object. If a bounding box prior is not assigned to a groundtruth object it incurs no loss for coordinate or class predictions, only objectness.

2.2. Class Prediction
Each box predicts the classes the bounding box may contain using multilabel classification. We do not use a softmax
as we have found it is unnecessary for good performance,
instead we simply use independent logistic classifiers. During training we use binary cross-entropy loss for the classpredictions.
This formulation helps when we move to more complex
domains like the Open Images Dataset [7]. In this dataset
there are many overlapping labels (i.e. Woman and Person).
Using a softmax imposes the assumption that each box has
exactly one class which is often not the case. A multilabel
approach better models the data.

2.3. Predictions Across Scales
YOLOv3 predicts boxes at 3 different scales. Our system extracts features from those scales using a similar concept to feature pyramid networks [8]. From our base feature extractor we add several convolutional layers. The lastof these predicts a 3-d tensor encoding bounding box, objectness, and class predictions. In our experiments with
COCO [10] we predict 3 boxes at each scale so the tensor is
N × N × [3 ∗ (4 + 1 + 80)] for the 4 bounding box offsets,
1 objectness prediction, and 80 class predictions.

We use a new network for performing feature extraction.
Our new network is a hybrid approach between the network
used in YOLOv2, Darknet-19, and that newfangled residual
network stuff. Our network uses successive 3 × 3 and 1 × 1
convolutional layers but now has some shortcut connections
as well and is significantly larger. It has 53 convolutional
layers so we call it.... wait for it..... Darknet-53!
OutputType
Filters Size
256 × 256
Convolutional 32 3 × 3
Convolutional 64 3 × 3 / 2 128 × 128
Convolutional 32 1 × 1
1× Convolutional 64 3 × 3
128 × 128
Residual
Convolutional 128 3 × 3 / 2 64 × 64
Convolutional 64 1 × 1
2× Convolutional 128 3 × 3
64 × 64
Residual
Convolutional 256 3 × 3 / 2 32 × 32
Convolutional 128 1 × 1
8× Convolutional 256 3 × 3
32 × 32
Residual
Convolutional 512 3 × 3 / 2 16 × 16
Convolutional 256 1 × 1
8× Convolutional 512 3 × 3
16 × 16
Residual
8×8
Convolutional 1024 3 × 3 / 2
Convolutional 512 1 × 1
4× Convolutional 1024 3 × 3
8×8
Residual
GlobalAvgpool
1000
Connected
Softmax

Table 1. Darknet-53.

This new network is much more powerful than Darknet19 but still more efficient than ResNet-101 or ResNet-152.
Here are some ImageNet results:
Backbone
Darknet-19 [15]ResNet-101[5]ResNet-152 [5]Darknet-53

Top-1
74.1
77.1
77.6
77.2

Top-5
91.8
93.7
93.8
93.8

Bn Ops
7.29
19.7
29.4
18.7

BFLOP/s
1246
1039
1090
1457

FPS
171
53
37
78

Table 2. Comparison of backbones. Accuracy, billions of operations, billion floating point operations per second, and FPS forvarious networks.

Each network is trained with identical settings and tested
at 256 × 256, single crop accuracy. Run times are measured
on a Titan X at 256 × 256. Thus Darknet-53 performs on
par with state-of-the-art classifiers but with fewer floating
point operations and more speed. Darknet-53 is better than
ResNet-101 and 1.5× faster. Darknet-53 has similar performance to ResNet-152 and is 2× faster.
Darknet-53 also achieves the highest measured floating
point operations per second. This means the network structure better utilizes the GPU, making it more efficient to evaluate and thus faster. That’s mostly because ResNets havejust way too many layers and aren’t very efficient.

2.5. Training
We still train on full images with no hard negative mining
or any of that stuff. We use multi-scale training, lots of data
augmentation, batch normalization, all the standard stuff.
We use the Darknet neural network framework for training
and testing [14].

3. How We Do
YOLOv3 is pretty good! See table 3. In terms of COCOs
weird average mean AP metric it is on par with the SSD
variants but is 3× faster. It is still quite a bit behind otherTwo-stage methods
Faster R-CNN+++ [5]Faster R-CNN w FPN [8]Faster R-CNN by G-RMI [6]Faster R-CNN w TDM [20]One-stage methods
YOLOv2 [15]SSD513 [11, 3]
DSSD513 [3]RetinaNet [9]RetinaNet [9]YOLOv3 608 × 608

models like RetinaNet in this metric though.
However, when we look at the “old” detection metric ofmAP at IOU= .5 (or AP50 in the chart) YOLOv3 is verystrong. It is almost on par with RetinaNet and far above
the SSD variants. This indicates that YOLOv3 is a verystrong detector that excels at producing decent boxes for objects. However, performance drops significantly as the IOU
threshold increases indicating YOLOv3 struggles to get theboxes perfectly aligned with the object.
In the past YOLO struggled with small objects. However, now we see a reversal in that trend. With the newmulti-scale predictions we see YOLOv3 has relatively high
APS performance. However, it has comparatively worse
performance on medium and larger size objects. More investigation is needed to get to the bottom of this.
When we plot accuracy vs speed on the AP50 metric (seefigure 5) we see YOLOv3 has significant benefits over otherdetection systems. Namely, it’s faster and better.

4. Things We Tried That Didn’t WorkWe tried lots of stuff while we were working on
YOLOv3. A lot of it didn’t work. Here’s the stuff we canremember.
Anchor box x, y offset predictions. We tried using thenormal anchor box prediction mechanism where you predict the x, y offset as a multiple of the box width or heightusing a linear activation. We found this formulation decreased model stability and didn’t work very well.
Linear x, y predictions instead of logistic. We tried
using a linear activation to directly predict the x, y offset
instead of the logistic activation. This led to a couple point
drop in mAP.
Focal loss. We tried using focal loss. It dropped ourmAP about 2 points. YOLOv3 may already be robust to
the problem focal loss is trying to solve because it has separate objectness predictions and conditional class predictions. Thus for most examples there is no loss from theclass predictions? Or something? We aren’t totally sure.

backbone

AP

AP50

AP75

APS

APM

APL

ResNet-101-C4
ResNet-101-FPN
Inception-ResNet-v2 [21]Inception-ResNet-v2-TDM

34.9
36.2
34.7
36.8

55.7
59.1
55.5
57.7

37.4
39.0
36.7
39.2

15.6
18.2
13.5
16.2

38.7
39.0
38.1
39.8

50.9
48.2
52.0
52.1

DarkNet-19 [15]ResNet-101-SSD
ResNet-101-DSSD
ResNet-101-FPN
ResNeXt-101-FPN
Darknet-53

21.6
31.2
33.2
39.1
40.8
33.0

44.0
50.4
53.3
59.1
61.1
57.9

19.2
33.3
35.2
42.3
44.1
34.4

5.0
10.2
13.0
21.8
24.1
18.3

22.4
34.5
35.4
42.7
44.2
35.4

35.5
49.8
51.1
50.2
51.2
41.9

Table 3. I’m seriously just stealing all these tables from [9] they take soooo long to make from scratch. Ok, YOLOv3 is doing alright.
Keep in mind that RetinaNet has like 3.8× longer to process an image. YOLOv3 is much better than SSD variants and comparable tostate-of-the-art models on the AP50 metric.

G

58

COCO mAP-50

56
54

F

52

D
E

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48
50

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C

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YOLOv3RetinaNet-50
RetinaNet-101

mAP-50 time
Method61
45.4[B] SSD321
85
46.1[C] DSSD32185
51.9[D] R-FCN
125
50.4[E] SSD513
156
53.3[F] DSSD513
172
59.1[G] FPN FRCN
73
50.9
RetinaNet-50-500
90RetinaNet-101-500 53.1
198
RetinaNet-101-800 57.5
22
YOLOv3-320
51.5
55.3
29
YOLOv3-416
57.9
51
YOLOv3-608200

inference time (ms)250

Figure 3. Again adapted from the [9], this time displaying speed/accuracy tradeoff on the mAP at .5 IOU metric. You can tell YOLOv3 isgood because it’s very high and far to the left. Can you cite your own paper? Guess who’s going to try, this guy → [16]. Oh, I forgot, we
also fix a data loading bug in YOLOv2, that helped by like 2 mAP. Just sneaking this in here to not throw off layout.

Dual IOU thresholds and truth assignment. Faster RCNN uses two IOU thresholds during training. If a prediction overlaps the ground truth by .7 it is as a positive example, by [.3 − .7] it is ignored, less than .3 for all ground truth
objects it is a negative example. We tried a similar strategy
but couldn’t get good results.
We quite like our current formulation, it seems to be at
a local optima at least. It is possible that some of thesetechniques could eventually produce good results, perhaps
they just need some tuning to stabilize the training.

5. What This All Means
YOLOv3 is a good detector. It’s fast, it’s accurate. It’s
not as great on the COCO average AP between .5 and .95
IOU metric. But it’s very good on the old detection metricof .5 IOU.
Why did we switch metrics anyway? The original
COCO paper just has this cryptic sentence: “A full discussion of evaluation metrics will be added once the evaluation
server is complete”. Russakovsky et al report that that humans have a hard time distinguishing an IOU of .3 from .5!
“Training humans to visually inspect a bounding box withIOU of 0.3 and distinguish it from one with IOU 0.5 is sur-prisingly difficult.” [18] If humans have a hard time tellingthe difference, how much does it matter?
But maybe a better question is: “What are we going to
do with these detectors now that we have them?” A lot of
the people doing this research are at Google and Facebook.
I guess at least we know the technology is in good hands
and definitely won’t be used to harvest your personal information and sell it to.... wait, you’re saying that’s exactly
what it will be used for?? Oh.
Well the other people heavily funding vision research are
the military and they’ve never done anything horrible likekilling lots of people with new technology oh wait.....1
I have a lot of hope that most of the people using computer vision are just doing happy, good stuff with it, likecounting the number of zebras in a national park [13], ortracking their cat as it wanders around their house [19]. But
computer vision is already being put to questionable use andas researchers we have a responsibility to at least consider
the harm our work might be doing and think of ways to mitigate it. We owe the world that much.
In closing, do not @ me. (Because I finally quit Twitter).
1 The author is funded by the Office of Naval Research and Google.

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338, 2010. 6[3] C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg.
Dssd: Deconvolutional single shot detector. arXiv preprint
arXiv:1701.06659, 2017. 3[4] D. Gordon, A. Kembhavi, M. Rastegari, J. Redmon, D. Fox,
and A. Farhadi. Iqa: Visual question answering in interactive
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770–778, 2016. 3[6] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara,
A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al.
Speed/accuracy trade-offs for modern convolutional object
detectors. 3[7] I. Krasin, T. Duerig, N. Alldrin, V. Ferrari, S. Abu-El-Haija,
A. Kuznetsova, H. Rom, J. Uijlings, S. Popov, A. Veit,
S. Belongie, V. Gomes, A. Gupta, C. Sun, G. Chechik,D. Cai, Z. Feng, D. Narayanan, and K. Murphy. Openimages: A public dataset for large-scale multi-label andmulti-class image classification. Dataset available fromhttps://github.com/openimages, 2017. 2[8] T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and
S. Belongie. Feature pyramid networks for object detection.
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vision, pages 740–755. Springer, 2014. 2[11] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, and A. C. Berg. Ssd: Single shot multibox detector.
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Springer, 2016. 3[12] I. Newton. Philosophiae naturalis principia mathematica.
William Dawson & Sons Ltd., London, 1687. 1[13] J. Parham, J. Crall, C. Stewart, T. Berger-Wolf, andD. Rubenstein. Animal population censusing at scale withcitizen science and photographic identification. 2017. 4[14] J. Redmon. Darknet: Open source neural networks in c.
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Inception-v4, inception-resnet and the impact of residual
connections on learning. 2017. 3

100

100

75

75

All the other slow ones50

mAP 50mAP 50

YOLOv325

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0

All the other slow ones

YOLOv3

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FPS

50

Figure 4. Zero-axis charts are probably more intellectually honest... and we can still screw with the variables to make ourselves look good!

Rebuttal
We would like to thank the Reddit commenters, labmates,
emailers, and passing shouts in the hallway for their lovely, heartfelt words. If you, like me, are reviewing for ICCV then we know
you probably have 37 other papers you could be reading that you’ll
invariably put off until the last week and then have some legend in
the field email you about how you really should finish those reviews execept it won’t entirely be clear what they’re saying andmaybe they’re from the future? Anyway, this paper won’t have become what it will in time be without all the work your past selves
will have done also in the past but only a little bit further forward,
not like all the way until now forward. And if you tweeted about
it I wouldn’t know. Just sayin.
Reviewer #2 AKA Dan Grossman (lol blinding who does that)
insists that I point out here that our graphs have not one but two
non-zero origins. You’re absolutely right Dan, that’s because it
looks way better than admitting to ourselves that we’re all justhere battling over 2-3% mAP. But here are the requested graphs.
I threw in one with FPS too because we look just like super goodwhen we plot on FPS.
Reviewer #4 AKA JudasAdventus on Reddit writes “Entertaining read but the arguments against the MSCOCO metrics seem abit weak”. Well, I always knew you would be the one to turn onme Judas. You know how when you work on a project and it only
comes out alright so you have to figure out some way to justify
how what you did actually was pretty cool? I was basically trying
to do that and I lashed out at the COCO metrics a little bit. But
now that I’ve staked out this hill I may as well die on it.
See here’s the thing, mAP is already sort of broken so an update to it should maybe address some of the issues with it or at leastjustify why the updated version is better in some way. And that’s
the big thing I took issue with was the lack of justification. For
PASCAL VOC, the IOU threshold was ”set deliberately low to account for inaccuracies in bounding boxes in the ground truth data“[2]. Does COCO have better labelling than VOC? This is definitely possible since COCO has segmentation masks maybe thelabels are more trustworthy and thus we aren’t as worried about
inaccuracy. But again, my problem was the lack of justification.
The COCO metric emphasizes better bounding boxes but thatemphasis must mean it de-emphasizes something else, in this caseclassification accuracy. Is there a good reason to think that more

precise bounding boxes are more important than better classification? A miss-classified example is much more obvious than abounding box that is slightly shifted.
mAP is already screwed up because all that matters is per-classrank ordering. For example, if your test set only has these two
images then according to mAP two detectors that produce theseresults are JUST AS GOOD:
Detector #1
Bird: 99%
Person: 99%

Person: 99%

Camel: 99%

Dog: 99%

Horse: 99%

Detector #2
Bird: 90%

Horse: 52%
Person: 42%

Horse: 60%

Bird: 89%

Dog: 48%

Dog: 45%
Person: 11%

Camel: 10%

Horse: 70%

Bird: 75%

Figure 5. These two hypothetical detectors are perfect according tomAP over these two images. They are both perfect. Totally equal.
Now this is OBVIOUSLY an over-exaggeration of the problems with mAP but I guess my newly retconned point is that there
are such obvious discrepancies between what people in the “realworld” would care about and our current metrics that I think if
we’re going to come up with new metrics we should focus on
these discrepancies. Also, like, it’s already mean average precision, what do we even call the COCO metric, average mean average precision?
Here’s a proposal, what people actually care about is given an
image and a detector, how well will the detector find and classify
objects in the image. What about getting rid of the per-class AP
and just doing a global average precision? Or doing an AP calculation per-image and averaging over that?
Boxes are stupid anyway though, I’m probably a true believer
in masks except I can’t get YOLO to learn them.