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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
Gaurav Kumar Singh
about 1 month ago

Ford's Gesture Recognition System

96.77%
2
Anonim
3 months ago

96.74%
3
Anonymous
11 months ago

DRX3D

96.6%
4
Anonymous
about 2 months ago

96.56%
5
BOE_IOT_AIBD
28 days ago

TSN_two-stream aggregated with conv

96.37%
6
Okan Köpüklü
9 months 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%
7
Mohamad ALjazaery @Midea
6 months ago

Spatiotemporal Two Streams network

96.28%
8
Anonymous
6 months ago

3D CNN Architecture

96.24%
9
Anonymous
9 months ago

Motion Feature Network (MFNet)

96.22%
10
Anonymous
about 1 month ago

RNP

95.96%
11
Jingyao Wang
23 days ago

95.96%
12
anonymous
about 1 month ago

SSNet RGB resnet

95.79%
13
Anonymous
about 1 month ago

TVB

95.71%
14
Ke Yang (NUDT_PDL)
11 months ago

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

95.34%
15
Anonymous
11 months ago

DIN

95.31%
16
Guangming Zhu
12 months ago

95.01%
17
SJ
11 months ago

94.87%
18
Anonymous
about 1 year ago

TRN (CVPR'18 submission)

94.78%
19
Thomas Friedel
8 months ago

94.74%
20
ALAB
2 months ago

TRN + BNInception

94.5%
21
Eren Gölge
about 1 year ago

Besnet

94.23%
22
Guillaume Berger
about 1 year ago

93.87%
23
XM
3 days ago

ECO

93.82%
24
Francesco Dalla Serra
3 months ago

One Stream Modified-I3D

93.41%
25
Baptist
12 months ago

93.11%
26
Prasad Pai
1 day ago

91.36%
27
Wu Jie @ DLUT
about 1 month ago

C3D-like with delaying temporal pooling.

90.53%
28
Oskar Holmberg, Filip Granqvist
6 months ago

90.52%
29
anonymous
7 months ago

Modified C3D

89.26%
30
Haibing Huang
about 1 month ago

CNN+LSTM

86.31%
31
Arnaud Steinmetz
about 1 month ago

3D ResNet 101

85.99%
32
John Emmons
about 1 year ago

VideoLSTM

85.86%
33
Olivier Valery
about 2 months ago

3D convolutional neural network

85.49%
34
Wu Jie @ DLUT
about 1 month ago

C3D

84.23%
35
Damien MENIGAUX
about 1 year ago

ConvLSTM

82.76%
36
Joanna Materzyńska
over 1 year ago

Twenty Billion Neuron's Jester System

82.34%
37
ke yang(BUPT)
6 months ago

3D ResNet

68.13%
38
Victor
4 months ago

3D-ResNet101 trained on Kinetics

59.01%
39
Yu Zhu
over 1 year ago

10.52%
40
Ming
7 months ago

Test

4.0%
41
Konfuzius
over 1 year ago

Just random guessing...

3.65%



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