HaGRID – HAnd Gesture Recognition Image Dataset
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HaGRID – HAnd Gesture Recognition Image Dataset
Datasets
HaGRID - HAnd Gesture Recognition Image Dataset
File
HaGRID - HAnd Gesture Recognition Image Dataset
Use Case
Gesture Recognition
Description
The HaGRiD (Hand Gesture Recognition Image Dataset) is a collection of images and data designed for the development and evaluation of hand gesture recognition systems. Hand gesture recognition is a technology used in human-computer interaction, sign language recognition, and various other applications.
Description:
![HaGRID – HAnd Gesture Recognition Image Dataset](https://gts.ai/wp-content/uploads/2024/05/hgr_block_diagram-300x126-1.webp)
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.
![HaGRID – HAnd Gesture Recognition Image Dataset](https://gts.ai/wp-content/uploads/2024/05/0_qdHkFap6kzTBeCHo-1024x273.webp)
 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.Â
Annotations
[top left X position, top left Y position, width, height]
. Also, annotations have 21Â landmarks
 in format [x,y]
 relative image coordinates, 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.
"0534147c-4548-4ab4-9a8c-f297b43e8ffb": {
"bboxes": [
[0.38038597, 0.74085361, 0.08349486, 0.09142549],
[0.67322755, 0.37933984, 0.06350809, 0.09187757]
],
"landmarks"[
[
[
[0.39917091, 0.74502739],
[0.42500172, 0.74984396],
...
],
[0.70590734, 0.46012364],
[0.69208878, 0.45407018],
...
],
],
"labels": [
"no_gesture",
"one"
],
"leading_hand": "left",
"leading_conf": 1.0,
"user_id": "bb138d5db200f29385f..."
}
Download
Download and unzip our train dataset split into 18 archives by gestures. Get started with the links below
Trainval
GestureSize  Gesture           Size
call    39.1 GBpeace            38.6 GB
dislike  38.7 GBpeace_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 GBtrain_val
 annotations: ann_train_val
Test
Test Archives    Size
images test     60.4 GB
annotations ann_test 27.3 MB
It has 100 items per gesture.
 Archives       Size
images subsample        2.5 GB
annotations ann_subsample 1.2 ÐœB
Models
We provide some pre-trained classifiers and detectors as the baseline solutions. You can download them via github repo.
Links
Conclusion
Globose Technology Solutions excels in hand gesture recognition, utilizing the HaGRID dataset for precise identification. Our advanced algorithms contribute to innovative applications in human-computer interaction and virtual reality, reflecting our commitment to cutting-edge gesture recognition technology.
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