The dataset contains a comprehensive collection of human activity videos, spanning across 7 distinct classes. These classes include clapping, meeting and splitting, sitting, standing still, walking, walking while reading book, and walking while using the phone.

Each video clip in the dataset showcases a specific human activity and has been labeled with the corresponding class to facilitate supervised learning.

The primary inspiration behind creating this dataset is to enable machines to recognize and classify human activities accurately. With the advent of computer vision and deep learning techniques, it has become increasingly important to train machine learning models on large and diverse datasets to improve their accuracy and robustness.

Traffic Human Detection

## Overview

Traffic Human Detection is a dataset for object detection tasks - it contains Persons annotations for 1,332 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

🚢 Stanford Human Preferences Dataset (SHP)

If you mention this dataset in a paper, please cite the paper: Understanding Dataset Difficulty with V-Usable Information (ICML 2022).

  Summary

SHP is a dataset of 385K collective human preferences over responses to questions/instructions in 18 different subject areas, from cooking to legal advice. The preferences are meant to reflect the helpfulness of one response over another, and are intended to be used for training RLHF… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/SHP.

gsstein/100-percent-human-dataset-opt-1 dataset hosted on Hugging Face and contributed by the HF Datasets community

Human Or Not Human

## Overview

Human Or Not Human is a dataset for object detection tasks - it contains Objects annotations for 1,038 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

gsstein/75-percent-human-dataset-og dataset hosted on Hugging Face and contributed by the HF Datasets community

Human Detection Aerial View

## Overview

Human Detection Aerial View is a dataset for object detection tasks - it contains Humans annotations for 2,644 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

gsstein/0-percent-human-dataset-llama-og dataset hosted on Hugging Face and contributed by the HF Datasets community

Human

## Overview

Human is a dataset for object detection tasks - it contains Person annotations for 800 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

The Digital Human Market Report is Segmented by Product Type (Interactive Digital Human and Non-Interactive Digital Human), by Component (Software Platforms, and More), by Deployment Mode (Cloud-Based, and More), by End-User Industry (Retail and E-Commerce, Gaming and Entertainment, BFSI, and More), by Technology (Generative-AI Digital Humans, and More), and Geography.

The NexusStreets dataset contains human and autonomous driving scenes. They are collected by monitoring a target vehicle that can be either autonomous or controlled by a human driver. Data is presented in the shape of:

sequences of JPEG images, one image per timestamp

target vehicle state information for each timestamp

The dataset has been built on the CARLA simulator, thanks to Baidu Apollo and a Logitech G29 steering wheel for the autonomous and human drivings, respectively.The dataset consists of 520 scenes (260 pairs of mirrored scenarios) of 60 seconds each.The folders are organized as follows:

. ├── ... ├──
│ ├──
│ │ ├──
│ │ │ └── ...
│ │ └── ... │ └── ... └── ...

driving mode: corresponds to the control modality of the target vehicle under test and can be either Baidu Apollo or manual driving;

town: one of the five default maps in CARLA (e.g., Town01, Town02, etc);

trial: 60 different trials per map, they differ in traffic and weather conditions (except Town04). Each trial records 60 seconds of simulation, logging 120 frames per video and an equal number of rows per CSV. In particular, each trial includes:

video: this folder groups the JPEG images;

state_features.csv: reports the state information of the target vehicle for each frame;

detection_features.csv: reports the 2D bounding box detections obtained from a pre-trained YOLOv7 detector.

The HANPP Collection: Human Appropriation of Net Primary Productivity as a Percentage of Net Primary Productivity represents a map identifying regions in which human consumption of NPP is greatly in excess of production by local ecosystems. Humans appropriate net primary productivity through the consumption of food, paper, wood and fiber, which alters the composition of the atmosphere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services. Net primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems.

Introduction

Welcome to the Native American Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 5,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

The annual Country Reports on Human Rights Practices � the Human Rights Reports � cover internationally recognized individual, civil, political, and worker rights, as set forth in the Universal Declaration of Human Rights and other international agreements. The reports reflect a vast amount of objective research that provides a uniquely valuable resource for anybody in the world who cares about justice and law. It also equips interested observers with an arsenal of facts.

Human-Animal-Cartoon dataset

Our Human-Animal-Cartoon (HAC) dataset consists of seven actions (‘sleeping’, ‘watching tv’, ‘eating’, ‘drinking’, ‘swimming’, ‘running’, and ‘opening door’) performed by humans, animals, and cartoon figures, forming three different domains. We collect 3381 video clips from the internet with around 1000 for each domain and provide three modalities in our dataset: video, audio, and pre-computed optical flow. The dataset can be used for Multi-modal Domain… See the full description on the dataset page: https://huggingface.co/datasets/hdong51/Human-Animal-Cartoon.

torso

The Stanford Human Preferences (SHP) dataset is sourced from Reddit with various subreddits that focus on QA. Preferences have been extracted from the accumulated up- and down-votes of the online community.

1. Specifications Data size : 200,000 ID

Race distribution : Asians, Caucasians, black people

Gender distribution : gender balance

Age distribution : ranging from teenager to the elderly, the middle-aged and young people are the majorities

Collecting environment : including indoor and outdoor scenes

Data diversity : different shooting heights, different ages, different light conditions, different collecting environment, clothes in different seasons, multiple human poses

Device : cameras

Data format : the data format is .jpg/mp4, the annotation file format is .json, the camera parameter file format is .json, the point cloud file format is .pcd

Accuracy : based on the accuracy of the poses, the accuracy exceeds 97%;the accuracy of labels of gender, race, age, collecting environment and clothes are more than 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go machine learning (ML) data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at hhttps://www.nexdata.ai/datasets/computervision?source=Datarade

gsstein/50-percent-human-dataset-llama dataset hosted on Hugging Face and contributed by the HF Datasets community

ID:1 Human

## Overview

ID:1 Human is a dataset for object detection tasks - it contains Cnc annotations for 350 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).