Total Users 10,229,822 Total Pictures 10M+ (mostly 1 per ID)

Gender: - Male 60% - Female 40%

Ethnicity: - Asian 9% - African Decent 13% - East Indian 3% - Latino Hispanic 28% - Caucasian 47%

Age Group: - 0-17 3% - 18-24 62% - 25-34 21% - 35-44 10% - 45-54 3% - 55+ 1%

Top Phone Models: - iPhone 6s 9% - iPhone XR 6% - iPhone 6 6% - iPhone 7 (US/CDMA) 6% - iPhone 11 5% - iPhone 8 (US/CDMA) 4% (Total 141 device)

Top Countries: - US 48.84% - GB 10.57% - CA 4.26% - AU 3.48% - FR 2.80% - SA 2.17% (Total 131 countries)

Average resolution 5761024 px.

All photos are collected with the consent of users.

This dataset consists of 4.2 million (4,233,900 more precisely) geotagged images from the YFCC100M dataset. The images are from a subset of images used in MediaEval Placing Task 2016. For each image, its id, latitude and longitude where it was taken, plus the image itself, are stored as a record in MessagePack format.

Each shard file (a *.msg file) contains 30 thousand images.

An illustration of how each record looks like is shown below.

{'image': b'\xff\xd8\xff\xe0...
\x05\x87\xef\x1e\x94o\xf6\xa6QG\xb4\x90Xv\xfa7\xd3h\', 
'id': '13/20/8010869266.jpg', 'latitude': 29.426458, 'longitude': -98.490723}

Face Recognition, Face Detection, Female Photo Dataset 👩

  The dataset is created on the basis of Selfies and ID Dataset

90,000+ photos of 46,000+ women from 141 countries. The dataset includes photos of people's faces. All people presented in the dataset are women. The dataset contains a variety of images capturing individuals from diverse backgrounds and age groups. Our dataset will diversify your data by adding more photos of women of different ages and ethnic groups… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/female-selfie-image-dataset.

In the NTUT 4K Drone Photo Dataset for Human Detection authors furnish 4K photos extracted from drone videos captured in Taiwan. Authors claim, that contemporary drones are outfitted with 4K video cameras, and the heightened resolution of the images facilitates modern object detectors in discerning smaller objects. Despite this capability, many drone image datasets typically offer only downscaled images. The dataset is curated by the AIoT Lab at the National Taiwan University of Technology (NTUT).

Dataset

This dataset was created by Nuttida Lapthanachai

Contents

Image Dataset of face images for compuer vision tasks

Dataset comprises 500,600+ images of individuals representing various races, genders, and ages, with each person having a single face image. It is designed for facial recognition and face detection research, supporting the development of advanced recognition systems. By leveraging this dataset, researchers and developers can enhance deep learning models, improve face verification and face identification techniques, and refine… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-recognition-image-dataset.

Photo

## Overview

Photo is a dataset for classification tasks - it contains Photo annotations for 5,166 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).

MMID is a large-scale, massively multilingual dataset of images paired with the words they represent collected at the University of Pennsylvania. The dataset is doubly parallel: for each language, words are stored parallel to images that represent the word, and parallel to the word's translation into English (and corresponding images.)

Dataset Card for "AI-Generated-vs-Real-Images-Datasets"

More Information needed

Labeled Mouse images suitable for AI and computer vision.

🌟 Unlock the potential of advanced computer vision tasks with our comprehensive dataset comprising 15,000 high-quality images. Whether you're delving into segmentation, object detection, or image captioning, our dataset offers a diverse array of visual data to fuel your machine learning models.

🔍 Our dataset is meticulously curated to encompass a wide range of streams, ensuring versatility and applicability across various domains. From natural landscapes to urban environments, from wildlife to everyday objects, our collection captures the richness and diversity of visual content.

📊 Dataset Overview:

🔢 Image Details:

• Format: JPG
• Size Range: Approximately 150 to 300 KB per image

Embark on your computer vision journey and leverage our dataset to develop cutting-edge algorithms, advance research, and push the boundaries of what's possible in visual recognition tasks. Join us in shaping the future of AI-powered image analysis.

Introduction

Welcome to the Middle Eastern Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 1500 facial image sets of Middle Eastern individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

Open Images is a dataset of ~9M images that have been annotated with image-level labels and object bounding boxes.

The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images, making it the largest existing dataset with object location annotations. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (8.4 per image on average). Moreover, the dataset is annotated with image-level labels spanning thousands of classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('open_images_v4', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/open_images_v4-original-2.0.0.png" alt="Visualization" width="500px">

Labeled Mountain images suitable for AI and computer vision.

This dataset collection scenarios include indoor and outdoor scenes, the country distribution is Algeria, Egypt, Hungary, Poland, and Japan. Data types include portrait photos and wedding photos. In terms of data annotation, detailed retouching and annotationing are performed on the collected studio portrait data. The data can be used for tasks such as raining models for portrait retouching, photo editing, and studio photography applications.

The BIQ2021 dataset is a large-scale blind image quality assessment database, consisting of 12,000 authentically distorted images. Each image in the dataset has been quality rated by 30 observers, resulting in a total of 360,000 quality ratings. This dataset was created in a controlled laboratory environment, ensuring consistent and reliable subjective scoring. Moreover, the dataset provide a train/test split by which the researchers can report their results for benchmarking. The dataset is openly available and serves as a valuable resource for evaluating and benchmarking image quality assessment algorithms. The paper providing a detailed description of the dataset and its creation process is openly accessible at the following link: BIQ2021: A large-scale blind image quality assessment database.

The paper can be sited as:

Ahmed, N., & Asif, S. (2022). BIQ2021: a large-scale blind image quality assessment database. Journal of Electronic Imaging, 31(5), 053010.

Dataset Description

Images: The dataset contain a folder named images containing 12,000 images to be used for training and testing. Train (Images and MOS): It is a CSV file containing randomly partitioned train set of the dataset containing 10,000 images with their corresponding MOS. Test (Images and MOS): It is a CSV file containing randomly partitioned test set of the dataset containing 2,000 images with their corresponding MOS.

Benchmarking: In order to compare the performance of a predictive model trained on the dataset, Pearson and Spearman's correlation can be computed and compared with the existing approaches and the CNN models listed at the following gitHub repository: https://github.com/nisarahmedrana/BIQ2021

Labeled Bull riding images suitable for AI and computer vision.

Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that are now archived at the Earth Data Analysis Center.

Images and source code for the publication.The archive contains all the images and the source code used for the analysis in the referenced publication. It also contains the obtained equality tables, such that the whole comparison does not need to be run again. Moreover, code for analyzing the results is provided. Refer to the contained readme.html file for more detailed information.comparisonOfPhotoMatchingAlgorithms.tar.gz

Curated RGB image dataset for our analysis, splited into training and evalutaion set. Based on ImageNet ILSVRC dataset (Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, 2015).