The 20BN-jester Dataset V1


Introduction

The 20BN-JESTER dataset is a large collection of labeled video clips that show humans performing pre-definded hand gestures in front of a laptop camera or webcam. The dataset was created by a large number of crowd workers. It allows for training robust machine learning models to recognize human hand gestures. It is available free of charge for academic research. Commercial licenses are available upon request.

A paper with supplementary material can be found here.

Sliding Two Fingers Down
Swiping Left
Thumb Up

Data format

The video data is provided as one large TGZ archive, split into parts of 1 GB max. The total download size is 22.8 GB. The archive contains directories numbered from 1 to 148092. Each directory corresponds to one video and contains JPG images with height 100px and variable width. The JPG images were extracted from the orginal videos at 12 frames per seconds. The filenames of the JPGs start at 00001.jpg. The number of JPGs varies as the length of the original videos varies.


20BN-JESTER-DATASET
Total number of videos
148,092
Training Set
118,562
Validation Set
14,787
Test Set (w/o labels)
14,743
Labels
27
Classes
12,416
Doing other things
5,460
Thumb Down
5,457
Thumb Up
5,444
Drumming Fingers
5,434
Pushing Hand Away
5,413
Stop Sign
5,410
Sliding Two Fingers Down
5,379
Pulling Hand In
5,379
Zooming Out With Two Fingers
5,358
Pushing Two Fingers Away
5,355
Zooming In With Two Fingers
5,345
Sliding Two Fingers Left
5,344
No gesture
5,330
Zooming Out With Full Hand
5,315
Pulling Two Fingers In
5,314
Shaking Hand
5,307
Zooming In With Full Hand
5,303
Swiping Down
5,262
Sliding Two Fingers Up
5,244
Sliding Two Fingers Right
5,240
Swiping Up
5,165
Rolling Hand Forward
5,160
Swiping Left
5,066
Swiping Right
5,031
Rolling Hand Backward
4,181
Turning Hand Counterclockwise
3,980
Turning Hand Clockwise

Available Licenses

Twenty Billion Neurons offers our Crowd Acting™ video dataset collections in three different license types depending on the organization you belong to and the intended use for the data.

Free Academic License

Academic institutions and non-profit organizations only.

Proceed

Corporate Research License

Perform research and evaluations in a corporate research lab or for-profit organization.

Proceed

Full Commercial License

Build the next level commercial A.I. products and services.

Proceed

Leaderboard

If you have been successful in creating a classification model based on the training set and it performs well on the validation set, we encourage you to run your model on the test set (which is published without any class labels, as you might have noticed). Please prepare a .csv file with the video's id in the first column and your predicted class label (as a string matching the wording used in the training and validation sets). As a separator, please use a semicolon. You can then upload your .csv file here (user login required) to be ranked in the leaderboard and to benchmark your approach against that of other machine learners. We are looking forward to your submission.

Rank
Name
Approach
Score
1
TmallGenie GestureWYS
about 2 months ago

WYS

97.37%
2
Iflytek
about 1 year ago

MobileNet+NL+SlowFast

97.26%
3
ysge
4 months ago

ysge

97.23%
4
BOE_IOT_AIBD
over 1 year ago

Fusion_TSN_LSTM

97.09%
5
Anonymous
over 1 year ago

CVPR2020Submission

97.09%
6
Huawei Noah's Ark Toronto Laboratory Team
almost 2 years ago

RFEEN, 20 Crops

97.06%
7
zhang yuan
over 1 year ago

MobileNet_v2_NL_16sample

97.04%
8
Anonymous
12 months ago

12F rgb

96.99%
9
Anonymous
over 1 year ago

rgb 12F

96.95%
10
Gaurav Kumar Singh
over 2 years ago

Ford's Gesture Recognition System

96.77%
11
Anonim
over 2 years ago

96.74%
12
NLPR-CASIA-LSHI
about 3 years ago

L. Shi, Y. Zhang, C. Jian, and L. Hanqing, "Gesture Recognition using Spatiotemporal Deformable Convolutional Representation" in IEEE International Conference on Image Processing (ICIP), 2019.

96.6%
13
Anonymous
over 2 years ago

96.56%
14
fengye
about 2 months ago

Deep fusion model

96.41%
15
Okan Köpüklü
about 3 years ago

Motion Fused Frames (MFFs)
Code: https://github.com/okankop/MFF-pytorch
Article: https://arxiv.org/pdf/1804.07187.pdf
Contact:okankopuklu@gmail.com

96.28%
16
Mohamad ALjazaery @Midea
almost 3 years ago

Spatiotemporal Two Streams network

96.28%
17
Anonymous
almost 3 years ago

3D CNN Architecture

96.24%
18
Anonymous
about 3 years ago

Motion Feature Network (MFNet)

96.22%
19
Villa
9 months ago

96.16%
20
guandai
about 2 months ago

96.11%
21
Anonymous
over 2 years ago

RNP

95.96%
22
Jingyao Wang
over 2 years ago

95.96%
23
jiaming huang
over 1 year ago

95.94%
24
Anonymous
almost 2 years ago

ResNext 101

95.87%
25
anonymous
over 2 years ago

SSNet RGB resnet

95.79%
26
Anonymous
over 1 year ago

95.79%
27
煌彬 吴
about 1 year ago

95.74%
28
Anonymous
about 1 year ago

95.74%
29
Wenjin Zhang, Jiacun Wang
about 1 year ago

Short-Term Sampling Neural Networks (9 groups)

95.73%
30
Anonymous
over 2 years ago

TVB

95.71%
31
Ke Yang (NUDT_PDL)
over 3 years ago

Temporal Pyramid Relation Network for Video-Based Gesture Recognition,2018 25th IEEE International Conference on Image Processing (ICIP)

95.34%
32
Anonymous
about 3 years ago

DIN

95.31%
33
Anonymous
about 2 years ago

95.14%
34
Guangming Zhu
over 3 years ago

95.01%
35
minivision_art
about 1 year ago

94.97%
36
Test
almost 2 years ago

TRN - 8 segments

94.95%
37
lilh
about 1 year ago

94.93%
38
Anonymous
about 1 year ago

94.88%
39
Wenjin Zhang
about 1 year ago

Short-Term Sampling Neural Networks

94.88%
40
SJ
about 3 years ago

94.87%
41
Roy Amante Salvador
about 2 years ago

3D CNN - Multi time scale evaluation

94.85%
42
Anonymous
about 2 years ago

8frames rgb

94.81%
43
Anonymous
over 3 years ago

TRN (CVPR'18 submission)

94.78%
44
Thomas Friedel
almost 3 years ago

94.74%
45
ALAB
over 2 years ago

TRN + BNInception

94.5%
46
Anonymous
almost 2 years ago

Anonymous

94.49%
47
Anonymous
about 2 years ago

final test
label+string

94.47%
48
shi huiying
5 months ago

94.36%
49
Roy Salvador
about 2 years ago

3D CNN for transfer learning

94.26%
50
Eren Gölge
over 3 years ago

Besnet

94.23%
51
Wu Jie @ DLUT-SIE
about 2 years ago

3D_GesNet

93.99%
52
Wu Jie @ DLUT-SIE
about 2 years ago

3D-GesNet(only rgb)

93.99%
53
Guillaume Berger
over 3 years ago

93.87%
54
XM
over 2 years ago

ECO

93.82%
55
ming chen
over 1 year ago

2D and 3D fused network

93.8%
56
Anonymous
about 2 years ago

TRN-E

93.58%
57
Zubair
over 1 year ago

Inceptionv2 - TRN16

93.55%
58
xxx
8 months ago

Test 1

93.47%
59
long
8 months ago

test

93.47%
60
666 212
8 months ago

test rgb 16f

93.47%
61
Francesco Dalla Serra
over 2 years ago

One Stream Modified-I3D

93.41%
62
DIVA
8 months ago

TAN_ep2

93.39%
63
199_3D
over 1 year ago

199_3D

93.12%
64
l lln
over 1 year ago

p3d_199

93.12%
65
Baptist
over 3 years ago

93.11%
66
Fábio Baldissera
almost 2 years ago

RT C3D - 16 Frames
Code: https://github.com/fabiopk

92.65%
67
INF
8 months ago

TPP sub1

92.52%
68
RyMult
over 1 year ago

91.61%
69
rml
over 1 year ago

91.61%
70
Prasad Pai
over 2 years ago

91.45%
71
Zhaoli Deng
9 months ago

91.39%
72
Jerry Tom
9 months ago

经典双赢

91.39%
73
wuxia tu
over 1 year ago

prune_net

91.22%
74
63_3D
over 1 year ago

63_3D

91.19%
75
tu tututu
over 1 year ago

result_3Dconv_63

91.19%
76
Yuriy
about 2 months ago

mobilenetv2_1.00_160, Dense, Dense

90.84%
77
Oskar Holmberg, Filip Granqvist
almost 3 years ago

90.52%
78
anonymous
almost 3 years ago

Modified C3D

89.26%
79
qi yuan
over 2 years ago

87.94%
80
Haibing Huang
over 2 years ago

CNN+LSTM

86.31%
81
Anonymous
about 1 year ago

3D CNN

86.15%
82
Zhiyuan Ma
over 1 year ago

86.07%
83
Arnaud Steinmetz
over 2 years ago

3D ResNet 101

85.99%
84
John Emmons
over 3 years ago

VideoLSTM

85.86%
85
Olivier Valery
over 2 years ago

3D convolutional neural network

85.49%
86
qiu feng
over 1 year ago

Result_63

85.31%
87
Damien MENIGAUX
over 3 years ago

ConvLSTM

82.76%
88
Raghul Krishna
about 1 year ago

82.53%
89
Joanna Materzyńska
almost 4 years ago

Twenty Billion Neuron's Jester System

82.34%
90
lbjbx
almost 2 years ago

3d+resnet18

81.55%
91
Jakub B.
3 months ago

ColumnConv3D

78.74%
92
Wei-Cheng Lin
over 1 year ago

77.89%
93
Liam Schoneveld
over 1 year ago

Basic finetune MobileNetV2 (pretrained imagenet) + LSTM output

74.4%
94
GY@CAD
over 1 year ago

interframe-difference LSTM

73.22%
95
yuzhe zhou
over 1 year ago

Mobilenetv3-LSTM

71.72%
96
ke yang(BUPT)
almost 3 years ago

3D ResNet

68.13%
97
John Waithaka
5 months ago

CNN-LSTM

63.0%
98
Victor
over 2 years ago

3D-ResNet101 trained on Kinetics

59.01%
99
CS2R_Muneer
over 1 year ago

57.04%
100
PortlyBoi
4 months ago

3 Temporal Stream Network + ConvRNN

48.23%
101
FeiHu Jiang
over 1 year ago

2D+3D

44.04%
102
James Zhang
about 1 month ago

2-Frame Difference with Otsu+One Stream I3D

39.37%
103
Yu Zhu
over 3 years ago

10.52%
104
Ming
almost 3 years ago

Test

4.0%
105
Anonymous
about 2 years ago

3.97%
106
Anonymous
about 2 years ago

rgb_only
test label (from 0 to 26)

0.0%
107
Anonymous
almost 2 years ago

ResNext 101

0.0%
108
Fabio B.
almost 2 years ago

[test_run] 3D RGB 16F

0.0%
109
ozgur
over 1 year ago

0.0%



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