Dataset Card for HaGRID - HAnd Gesture Recognition Image Dataset

Dataset Summary

We introduce a large image dataset HaGRID (HAnd Gesture Recognition Image Dataset) for hand gesture recognition (HGR) systems. You can use it for image classification or image detection tasks. Proposed dataset allows to build HGR systems, which can be used in video conferencing services (Zoom, Skype, Discord, Jazz etc.), home automation systems, the automotive sector, etc.

HaGRID size is 716GB and dataset contains 552,992 FullHD (1920 × 1080) RGB images divided into 18 classes of gestures. Also, some images have no_gesture class if there is a second free hand in the frame. This extra class contains 123,589 samples. The data were split into training 92%, and testing 8% sets by subject user-id, with 509,323 images for train and 43,669 images for test.

The dataset contains 34,730 unique persons and at least this number of unique scenes. The subjects are people from 18 to 65 years old. The dataset was collected mainly indoors with considerable variation in lighting, including artificial and natural light. Besides, the dataset includes images taken in extreme conditions such as facing and backing to a window. Also, the subjects had to show gestures at a distance of 0.5 to 4 meters from the camera.

Annotations

The annotations consist of bounding boxes of hands with gesture labels in COCO format [top left X position, top left Y position, width, height]. Also annotations have markups of leading hands (left of right for gesture hand) and leading_conf as confidence for leading_hand annotation. We provide user_id field that will allow you to split the train / val dataset yourself.

"03487280-224f-490d-8e36-6c5f48e3d7a0": {
  "bboxes": [
    [0.0283366, 0.8686061, 0.0757000, 0.1149820],
    [0.6824319, 0.2661254, 0.1086447, 0.1481245]
  ],
  "labels": [
    "no_gesture",
    "one"
  ],
  "leading_hand": "left",
  "leading_conf": 1.0,
  "user_id": "bb138d5db200f29385f..."
}

Downloads

We split the train dataset into 18 archives by gestures because of the large size of data. Download and unzip them from the following links:

Trainval

Gesture	Size	Gesture	Size
call	39.1 GB	peace	38.6 GB
dislike	38.7 GB	peace_inverted	38.6 GB
fist	38.0 GB	rock	38.9 GB
four	40.5 GB	stop	38.3 GB
like	38.3 GB	stop_inverted	40.2 GB
mute	39.5 GB	three	39.4 GB
ok	39.0 GB	three2	38.5 GB
one	39.9 GB	two_up	41.2 GB
palm	39.3 GB	two_up_inverted	39.2 GB

train_val annotations: ann_train_val

Test

Test	Archives	Size
images	test	60.4 GB
annotations	ann_test	3.4 MB

Subsample

Subsample has 100 items per gesture.

Subsample	Archives	Size
images	subsample	2.5 GB
annotations	ann_subsample	153.8 KB

Models

We provide some pre-trained classifiers and one detector as baselines.

Classifiers	F1 Gesture	F1 Leading hand
ResNet18	98.72	99.27
ResNet152	99.11	99.45
ResNeXt50	98.99	99.39
ResNeXt101	99.28	99.28
MobileNetV3-small	96.78	98.28
MobileNetV3-large	97.88	98.58
VitB-32	98.49	99.13

Detector	mAP
SSDLite	71.49

Links

• Github, Mirror
• arXiv
• Paperswithcode

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

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Data Fields

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Data Splits

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Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

This dataset was shared by @kapitanov

Licensing Information

The license for this dataset is cc-by-sa-4.0

Citation Information

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Contributions

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