Omkar2118/datasets huggingface dataset:
HaGRID
Full name: HaGRID - HAnd Gesture Recognition Image Dataset

We introduce a large image dataset **HaGRID** (**HA**nd **G**esture **R**ecognition **I**mage **D**ataset) 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 annotations consist of bounding boxes of hands in COCO format `[top left X position, top left Y position, width, height]` with gesture labels. Also, annotations have 21 `landmarks` in format `[x,y]` relative image coordinates, markups of `leading hands` (`left` or `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.

Introducing paper: HaGRID - HAnd Gesture Recognition Image Dataset