HaGRID - HAnd Gesture Recognition Image Dataset

We introduce a large image dataset HaGRIDv2 (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. We have also released an algorithm for dynamic gesture recognition, which we described in our paper. This model is trained entirely on HaGRIDv2 and enables the recognition of dynamic gestures while being trained exclusively on static ones. You can find it in our repository.

HaGRIDv2 size is 1.5T and dataset contains 1,086,158 FullHD RGB images divided into 33 classes of gestures and a new separate "no_gesture" class, containing domain-specific natural hand postures. Also, some images have no_gesture class if there is a second gesture-free hand in the frame. This extra class contains 2,164 samples. The data were split into training 76%, 9% validation and testing 15% sets by subject user_id, with 821,458 images for train, 99,200 images for validation and 165,500 for test.

The dataset contains 65,977 unique persons and at least this number of unique scenes. The subjects are people over 18 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.

Example of sample and its annotation:

For more information see our arxiv paper.

🔥 Changelog

• 2025/02/27: We release Dynamic Gesture Recognition algorithm. 🙋
  • Introduced a novel algorithm that enables dynamic gesture recognition while being trained exclusively on static gestures
  • Fully trained on the HaGRIDv2-1M dataset
  • Designed for real-time applications in video conferencing, smart home control, automotive systems, and more
  • Open-source implementation with pretrained models available in the repository
• 2024/09/24: We release HaGRIDv2. 🙏
  • The HaGRID dataset has been expanded with 15 new gesture classes, including two-handed gestures
  • New class "no_gesture" with domain-specific natural hand postures was addad (2,164 samples, divided by train/val/test containing 1,464, 200, 500 images, respectively)
  • Extra class no_gesture contains 200,390 bounding boxes
  • Added new models for gesture detection, hand detection and full-frame classification
  • Dataset size is 1.5T
  • 1,086,158 FullHD RGB images
  • Train/val/test split: (821,458) 76% / (99,200) 9% / (165,500) 15% by subject user_id
  • 65,977 unique persons
• 2023/09/21: We release HaGRID 2.0. ✌️
  • All files for training and testing are combined into one directory
  • The data was further cleared and new ones were added
  • Multi-gpu training and testing
  • Added new models for detection and full-frame classification
  • Dataset size is 723GB
  • 554,800 FullHD RGB images (cleaned and updated classes, added diversity by race)
  • Extra class no_gesture contains 120,105 samples
  • Train/val/test split: (410,800) 74% / (54,000) 10% / (90,000) 16% by subject user_id
  • 37,583 unique persons
• 2022/06/16: HaGRID (Initial Dataset) 💪
  • Dataset size is 716GB
  • 552,992 FullHD RGB images divided into 18 classes
  • Extra class no_gesture contains 123,589 samples
  • Train/test split: (509,323) 92% / (43,669) 8% by subject user_id
  • 34,730 unique persons from 18 to 65 years old
  • The distance is 0.5 to 4 meters from the camera

Installation

Clone and install required python packages:

git clone https://github.com/hukenovs/hagrid.git
# or mirror link:
cd hagrid
# Create virtual env by conda or venv
conda create -n gestures python=3.11 -y
conda activate gestures
# Install requirements
pip install -r requirements.txt

Downloads

We split the train dataset into 34 archives by gestures because of the large size of data. Download and unzip them from the following links:

Dataset

| Gesture | Size | Gesture | Size | Gesture | Size |-----------------------------------|---------|-------------------------------------------|---------|--------|----| | call | 37.2 GB | peace | 41.4 GB | grabbing | 48.7 GB | dislike | 40.9 GB | peace_inverted | 40.5 GB | grip | 48.6 GB | fist | 42.3 GB | rock | 41.7 GB | hand_heart | 39.6 GB | four | 43.1 GB | stop | 41.8 GB | hand_heart2 | 42.6 GB | like | 42.2 GB | stop_inverted | 41.4 GB | holy | 52.7 GB | mute | 43.2 GB | three | 42.2 GB | little_finger | 48.6 GB | ok | 42.5 GB | three2 | 40.2 GB | middle_finger | 50.5 GB | one | 42.7 GB | two_up | 41.8 GB | point | 50.4 GB | palm | 43.0 GB | two_up_inverted | 40.9 GB | take_picture | 37.3 GB | three3 | 54 GB | three_gun | 50.1 GB | thumb_index | 62.8 GB | thumb_index2 | 24.8 GB | timeout | 39.5 GB | xsign | 51.3 GB | no_gesture | 493.9 MB

dataset annotations: annotations

[HaGRIDv2 512px - lightweight version of the full dataset with](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/hagrid_v2/h