Omkar2118/datasets huggingface dataset:
WiGesture
Full name: Wireless Sensing Dataset for Gesture Recognition and People ID Identification with ESP32

WiGesture dataset contains data related to gesture recognition and people id identification in a meeting room scenario. The dataset provides synchronised CSI, RSSI, and timestamp for each sample. It can be used for research on WiFi-based human gesture recognition and people id identification.

Introducing paper: Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing
Radar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)

Abstract 

In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT-μRadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°).
DreamBooth++

DreamBench++ is a human-aligned benchmark for personalized image generation¹. It's automated by advanced multimodal GPT models¹. The goal of DreamBench++ is to assist humans in everyday work and life by creatively generating personalized content¹².

Here are some key features of DreamBench++:
- **Prompts Design**: The prompts are systematically designed to let GPT be both human-aligned and self-aligned¹.
- **Task Reinforcement**: The GPT models are empowered with task reinforcement¹.
- **Diverse Dataset**: A comprehensive dataset comprising diverse images and prompts is constructed¹.
- **Human-Aligned Evaluation**: By benchmarking 7 modern generative models, DreamBench++ results in significantly more human-aligned evaluation¹.

(1) DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation. https://dreambenchplus.github.io/.
(2) DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation. https://arxiv.org/abs/2406.16855.
(3) 图像生成评估基准DREAMBENCH++：与人类偏好对齐的 .... https://sd114.wiki/2676.html.
(4) DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation. https://arxiv.org/html/2406.16855v1.

Introducing paper: DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation