HaGRID – HAnd Gesture Recognition Image Dataset


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.

For more information see our arxiv paper HaGRID – HAnd Gesture Recognition Image Dataset.


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=3.9 -y
conda activate gestures 
# Install requirements
pip install -r requirements.txt

Docker Installation

docker build -t gestures .
docker run gestures


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:


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 Archives Size
images test 60.4 GB
annotations ann_test 3.4 MB


Subsample has 100 items per gesture.

Subsample Archives Size
images subsample 2.5 GB
annotations ann_subsample 153.8 KB

or by using python script

python download.py --save_path <PATH_TO_SAVE> \
                   --train \
                   --test \
                   --subset \
                   --annotations \

Run the following command with key --subset to download the small subset (100 images per class). You can download the train subset with --trainval or test subset with --test. Download annotations for selected stage by --annotations key. Download dataset with images by --dataset.

usage: download.py [-h] [--train] [--test] [--subset] [-a] [-d] [-t TARGETS [TARGETS ...]] [-p SAVE_PATH]

Download dataset...

optional arguments:
  -h, --help            show this help message and exit
  --train               Download trainval set
  --test                Download test set
  --subset              Download subset with 100 items of each gesture
  -a, --annotations     Download annotations
  -d, --dataset         Download dataset
  -t TARGETS [TARGETS ...], --targets TARGETS [TARGETS ...]
                        Target(s) for downloading train set
  -p SAVE_PATH, --save_path SAVE_PATH
                        Save path


We provide some pre-trained models as the baseline with the classic backbone architectures and two output heads – for gesture classification and leading hand classification.

Classifiers F1 Gestures 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
Vitb32 98.49 99.13

Also we provide SSDLite model with MobileNetV3 large backbone to solve hand detection problem.

Detector mAP
SSDLite 71.49


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": [
  "leading_hand": "left",
  "leading_conf": 1.0,
  "user_id": "bb138d5db200f29385f..."
  • Key – image name without extension
  • Bboxes – list of normalized bboxes [top left X pos, top left Y pos, width, height]
  • Labels – list of class labels e.g. like, stop, no_gesture
  • Leading hand – right or left for hand which showing gesture
  • Leading conf – leading confidence for leading_hand
  • User ID – subject id (useful for split data to train / val subsets).

Bounding boxes

Object Train + Val Test Total
gesture ~ 28 300 ~ 2 400 30 629
no gesture 112 740 10 849 123 589
total boxes 622 063 54 518 676 581


You can use downloaded trained models, otherwise select a classifier and parameters for training in default.yaml. To train the model, execute the following command:

python -m classifier/run.py --command 'train' --path_to_config <PATH>

Every step, the current loss, learning rate and others values get logged to Tensorboard. See all saved metrics and parameters by opening a command line (this will open a webpage at localhost:6006):

tensorboard --logdir=experiments


Test your model by running the following command:

python -m classifier/run.py --command 'test' --path_to_config <PATH>


python demo.py -p <PATH_TO_DETECTOR>



Creative Commons LicenseThis work is licensed under a variant of Creative Commons Attribution-ShareAlike 4.0 International License.

Please see the specific license.

Authors and Credits



You can cite the paper using the following BibTeX entry:

    title={HaGRID - HAnd Gesture Recognition Image Dataset},
    author={Kapitanov, Alexander and Makhlyarchuk, Andrey and Kvanchiani, Karina},
    journal={arXiv preprint arXiv:2206.08219},


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