Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
HAnd Gesture Recognition Image Dataset
| Date | Stars |
|---|---|
| 2026-07-24 | 1032 |
| 2026-07-25 | 1032 |
| 2026-07-28 | 1035 |
| 2026-07-30 | 1035 |
| 2026-08-06 | 1035 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# HaGRID - HAnd Gesture Recognition Image Dataset  We introduce a large image dataset **HaGRIDv2** (**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. 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](https://github.com/ai-forever/dynamic_gestures). 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](https://arxiv.org/abs/2412.01508). ## 🔥 Changelog - **`2025/02/27`**: We release [Dynamic Gesture Recognition algorithm](https://github.com/ai-forever/dynamic_gestures). 🙋 - 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](https://github.com/hukenovs/hagrid/tree/Hagrid_v2-1M). 🙏 - 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.](https://github.com/hukenovs/hagrid/tree/Hagrid_v2) ✌️ - 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)](https://github.com/hukenovs/hagrid/tree/Hagrid_v1) 💪 - Dataset size is **716GB** - **552,992** FullHD RGB i
Excerpt of 21,108 characters
Read on GitHub
27
17
6
6
2
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9f3ae29e3666e6e1, topic:computer-vision, topic:image-classification, readme:image classification
matched fp:9f3ae29e3666e6e1, topic:deep-learning
matched fp:9f3ae29e3666e6e1, topic:dataset, desc:dataset, readme:dataset