Cookies help us deliver our services. You can find more information in our Privacy Policy. Learn more




The 20BN-jester Dataset V1


Introduction

The 20BN-JESTER dataset is a large collection of densely-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.

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.

Terms of use

This dataset be used for academic research free of charge under the below license agreement. If you seek to use the data for commercial purposes please contact us.

Download Dataset

Please register or log in to download the dataset.


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
12,416
Doing other things
5,444
Drumming Fingers
5,344
No gesture
5,379
Pulling Hand In
5,315
Pulling Two Fingers In
5,434
Pushing Hand Away
5,358
Pushing Two Fingers Away
5,031
Rolling Hand Backward
5,165
Rolling Hand Forward
5,314
Shaking Hand
5,410
Sliding Two Fingers Down
5,345
Sliding Two Fingers Left
5,244
Sliding Two Fingers Right
5,262
Sliding Two Fingers Up
5,413
Stop Sign
5,303
Swiping Down
5,160
Swiping Left
5,066
Swiping Right
5,240
Swiping Up
5,460
Thumb Down
5,457
Thumb Up
3,980
Turning Hand Clockwise
4,181
Turning Hand Counterclockwise
5,307
Zooming In With Full Hand
5,355
Zooming In With Two Fingers
5,330
Zooming Out With Full Hand
5,379
Zooming Out With Two Fingers

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
Huawei Noah's Ark Toronto Laboratory Team
about 2 months ago

RFEEN, 20 Crops

97.06%
2
BOE_IOT_AIBD
11 days ago

Fusion_TSN

97.01%
3
Gaurav Kumar Singh
9 months ago

Ford's Gesture Recognition System

96.77%
4
Anonim
11 months ago

96.74%
5
Anonymous
over 1 year ago

DRX3D

96.6%
6
Anonymous
10 months ago

96.56%
7
Okan Köpüklü
over 1 year 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%
8
Mohamad ALjazaery @Midea
about 1 year ago

Spatiotemporal Two Streams network

96.28%
9
Anonymous
about 1 year ago

3D CNN Architecture

96.24%
10
Anonymous
over 1 year ago

Motion Feature Network (MFNet)

96.22%
11
Anonymous
9 months ago

RNP

95.96%
12
Jingyao Wang
9 months ago

95.96%
13
Anonymous
4 months ago

ResNext 101

95.87%
14
anonymous
9 months ago

SSNet RGB resnet

95.79%
15
Anonymous
10 months ago

TVB

95.71%
16
Ke Yang (NUDT_PDL)
over 1 year ago

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

95.34%
17
Anonymous
over 1 year ago

DIN

95.31%
18
Anonymous
5 months ago

95.14%
19
Guangming Zhu
over 1 year ago

95.01%
20
Test
3 months ago

TRN - 8 segments

94.95%
21
SJ
over 1 year ago

94.87%
22
Roy Amante Salvador
5 months ago

3D CNN - Multi time scale evaluation

94.85%
23
Anonymous
6 months ago

8frames rgb

94.81%
24
Anonymous
almost 2 years ago

TRN (CVPR'18 submission)

94.78%
25
Thomas Friedel
over 1 year ago

94.74%
26
ALAB
10 months ago

TRN + BNInception

94.5%
27
Anonymous
4 months ago

Anonymous

94.49%
28
Anonymous
6 months ago

final test
label+string

94.47%
29
Shuai
7 months ago

slowfast res50

94.46%
30
Roy Salvador
5 months ago

3D CNN for transfer learning

94.26%
31
Eren Gölge
almost 2 years ago

Besnet

94.23%
32
Wu Jie @ DLUT-SIE
5 months ago

3D_GesNet

93.99%
33
Wu Jie @ DLUT-SIE
4 months ago

3D-GesNet(only rgb)

93.99%
34
Guillaume Berger
almost 2 years ago

93.87%
35
XM
8 months ago

ECO

93.82%
36
Anonymous
5 months ago

TRN-E

93.58%
37
Francesco Dalla Serra
11 months ago

One Stream Modified-I3D

93.41%
38
Baptist
over 1 year ago

93.11%
39
Fábio Baldissera
about 2 months ago

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

92.65%
40
RyMult
15 days ago

91.61%
41
rml
14 days ago

91.61%
42
Prasad Pai
8 months ago

91.45%
43
Oskar Holmberg, Filip Granqvist
about 1 year ago

90.52%
44
anonymous
about 1 year ago

Modified C3D

89.26%
45
qi yuan
7 months ago

87.94%
46
Haibing Huang
10 months ago

CNN+LSTM

86.31%
47
Arnaud Steinmetz
10 months ago

3D ResNet 101

85.99%
48
John Emmons
almost 2 years ago

VideoLSTM

85.86%
49
Olivier Valery
10 months ago

3D convolutional neural network

85.49%
50
Damien MENIGAUX
over 1 year ago

ConvLSTM

82.76%
51
Joanna Materzyńska
about 2 years ago

Twenty Billion Neuron's Jester System

82.34%
52
lbjbx
3 months ago

3d+resnet18

81.55%
53
Liam Schoneveld
27 days ago

Basic finetune MobileNetV2 (pretrained imagenet) + LSTM output

74.4%
54
GY@CAD
8 days ago

interframe-difference LSTM

73.22%
55
yuzhe zhou
19 days ago

Mobilenetv3-LSTM

71.72%
56
ke yang(BUPT)
about 1 year ago

3D ResNet

68.13%
57
Victor
about 1 year ago

3D-ResNet101 trained on Kinetics

59.01%
58
Yu Zhu
about 2 years ago

10.52%
59
Ming
over 1 year ago

Test

4.0%
60
Anonymous
6 months ago

3.97%
61
Konfuzius
about 2 years ago

Just random guessing...

3.65%
62
Anonymous
6 months ago

rgb_only
test label (from 0 to 26)

0.0%
63
Anonymous
4 months ago

ResNext 101

0.0%
64
Fabio B.
2 months ago

[test_run] 3D RGB 16F

0.0%
65
ozgur
about 1 month ago

0.0%



Feedback