The UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc. Source: https://susanqq.github.io/UTKFace/ Image Source: https://susanqq.github.io/UTKFace/

MORPH is a facial age estimation dataset, which contains 55,134 facial images of 13,617 subjects ranging from 16 to 77 years old. Source: Deep Ordinal Regression Forests Image Source: https://uncw.edu/oic/tech/morph.html

To the best of our knowledge this is the largest publicly available dataset of face images with gender and age labels for training. We provide pretrained models for both age and gender prediction.

The Cross-Age Celebrity Dataset (CACD) contains 163,446 images from 2,000 celebrities collected from the Internet. The images are collected from search engines using celebrity name and year (2004-2013) as keywords. Therefore, it is possible to estimate the ages of the celebrities on the images by simply subtract the birth year from the year of which the photo was taken. Source: https://bcsiriuschen.github.io/CARC/ Image Source: https://www.pkuml.org/resources/pku-vehicleid.html

FGNet is a dataset for age estimation and face recognition across ages. It is composed of a total of 1,002 images of 82 people with age range from 0 to 69 and an age gap up to 45 years Source: Large age-gap face verification by feature injection in deep networks Image Source: https://www.researchgate.net/figure/Sample-images-from-the-FG-NET-Aging-database_fig1_220057621

Contains aesthetic scores and meaningful attributes assigned to each image by multiple human raters. Source: Photo Aesthetics Ranking Network with Attributes and Content Adaptation

Matanga Darknet — 2025 Access Guide As internet censorship intensifies, Shadow Marketplaces remain crucial tools for anonymous transactions. Matanga Darknet is one of the most reliable platforms offering secure deals, wide product selection, and user-friendly interface. This article explains how to access Matanga Darknet, its advantages, and security measures for darknet operations. --- Current mirrors for Matanga Darknet Search engines may block darknet resources, so we've compiled official and backup mirrors: 1. Clearnet mirror (if Tor unavailable): > https://mat2web.top (use with VPN!) 2. Official .onion address (Tor Browser only): > http://matanzkgpadqndp44ysejfdwehmy4m22mzevmicoth6ebzequny6ayid.onion/ 3. Backup domain (if main domain blocked): > https://matangaweb.com/ (verified URL) Important! Always verify site's PGP signature and avoid phishing clones. Additional links|mirrors Download Tor Browser > https://www.torproject.org/download/ What is Matanga Darknet? Matanga Darknet is a darknet marketplace operating on the Escrow (escrow) model that guarantees transaction security. Key features: - Anonymous payments (BTC+LTC) - No KYC (no identity verification) - Seller rating system and honest reviews --- How to access Matanga Darknet? 1. Install Tor Browser (<download>from official site]). 2. Disable JavaScript in browser settings to prevent leaks. 3. Use only verified mirrors (listed above). --- Advantages of Matanga Darknet over other marketplaces ? Wide selection of high-quality products ? PGP encryption for communications ? Automatic seller payouts --- How to avoid scammers? - Verify .onion addresses through forums (Dread, Telegram channels) - Don't follow links from emails/messengers - Use hardware wallets (Ledger, Trezor) --- Future of darknet marketplaces With the development of decentralization technologies (Freenet, I2P), Matanga Darknet plans to implement: - Fully p2p trading without a central server - NFT-based transaction guarantees --- Useful links https://flagyl4people.top/matanga-tbilisi-1.php Conclusion Matanga Darknet remains one of the safest darknet marketplaces in 2025. Use only verified mirrors and practice good digital hygiene to avoid blocks and fraud Matanga Guru Matanga Tbilisi Matanga Georgia Matanga Shop Matanga Pro Matanga Matanga Guru Tbilisi Matanga Be Matanga Ge Matanga Shop Tbilisi Matanga Ru Matanga Org Matanga Me Matanga Be Matanga Drugs Matanga Cc Matanga Onion Link Matanga Pw Onion Matanga Top Matanga Грузия Matanga Be Tbilisi Matanga Org В Обход Matanga Org Matanga B Matanga Batumi Matanga Be В Обход Блокировки Matanga De Matanga Dijo La Changa Matanga Drugs Tbilisi Matanga Gru Matanga Gruzia Matanga Hill Matanga Onion Matanga Pro Регистрация Matanga Pv Matanga Pw Matanga Pw В Обход Блокировки Matanga Registration Matanga Tor Приложение Matanga A-Pvp Crystal Matanga Guru Analogi Matanga Guru Cache Matanga Pro Gde Naiti Nomer Matanga How To Buy Drag On Matanga Matanga Be Matanga Guru На Matanga Be Matanga Shop Matanga Be Matanga Bariga Matanga Chat Matanga Club Matanga Dark Web Matanga Drug Matanga Drugs Site Matanga Exy Matanga Game On Steam Matanga Georgia Canabis Matanga Godnotaba Matanga Guru Ru Matanga Kokain Matanga Link Matanga Live Matanga Marixuana Matanga Market Matanga Means In Bengali Matanga Narkotiki Matanga Onion Links Matanga Org Onion Matanga Pw Обход Matanga Register Matanga Registracia Matanga Rp Matanga Ru Union Matanga Russia Matanga Shop Georgia Matanga Sx Matanga Tor Link Matanga Url Matanga Найди Все То Что Искал Matanga Shop Biz Matanga Be Onion Matanga Be Santamaria123456 Matanga Br Matanga Bve Matanga Eb Matanga Ffun Matanga Fn Matanga Fun Matanga G Matanga Ge ????????? Matanga Grou Matanga Gru Matanga Gur Matanga Guru Batumi Matanga Guru Tbilisi Matanga Gurua Matanga Gururu Matanga Guruu Matanga Guu Matanga Nt Matanga Onins Matanga Oru Matanga Png Matanga Pro Registration Matanga Pron Matanga Prp Matanga Sx Matanga Union Matanga Uru Matanga Wp Matanga Ww Matanga Wwp Matanga Xs Org Matanga Apk Rash Matanga Registration Matanga Ruonion Matanga Sites Like Matanga Matanga Be Visitor Authorize

The Asian Face Age Dataset (AFAD) is a new dataset proposed for evaluating the performance of age estimation, which contains more than 160K facial images and the corresponding age and gender labels. This dataset is oriented to age estimation on Asian faces, so all the facial images are for Asian faces. It is noted that the AFAD is the biggest dataset for age estimation to date. It is well suited to evaluate how deep learning methods can be adopted for age estimation.

AgeDB contains 16, 488 images of various famous people, such as actors/actresses, writers, scientists, politicians, etc. Every image is annotated with respect to the identity, age and gender attribute. There exist a total of 568 distinct subjects. The average number of images per subject is 29. The minimum and maximum age is 1 and 101, respectively. The average age range for each subject is 50.3 years. Source: AgeDB

The USF Human ID Gait Challenge Dataset is a dataset of videos for gait recognition. It has videos from 122 subjects in up to 32 possible combinations of variations in factors. Source: http://www.eng.usf.edu/cvprg/Gait_Data.html

MegaAge is a large dataset that consists of 41,941 faces annotated with age posterior distributions.

Data Description The training data contains twelve-lead ECGs. The validation and test data contains twelve-lead, six-lead, four-lead, three-lead, and two-lead ECGs: 1. Twelve leads: I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6 2. Six leads: I, II, III, aVR, aVL, aVF 3. Four leads: I, II, III, V2 4. Three leads: I, II, V2 5. Two leads: I, II Each ECG recording has one or more labels that describe cardiac abnormalities (and/or a normal sinus rhythm). We mapped the labels for each recording to SNOMED-CT codes. The lists of scored labels and unscored labels are given with the evaluation code; see the scoring section for details. # Data Sources The Challenge data include recordings from last year’s Challenge and many new recordings for this year’s Challenge: 1. CPSC Database and CPSC-Extra Database 2. INCART Database 3. PTB and PTB-XL Database 4. The Georgia 12-lead ECG Challenge (G12EC) Database 5. Augmented Undisclosed Database 6. Chapman-Shaoxing and Ningbo Database 7. The University of Michigan (UMich) Database The Challenge data include annotated twelve-lead ECG recordings from six sources in four countries across three continents. These databases include over 100,000 twelve-lead ECG recordings with over 88,000 ECGs shared publicly as training data, 6,630 ECGs retained privately as validation data, and 36,266 ECGs retained privately as test data. - The first source is the China Physiological Signal Challenge in 2018 (CPSC 2018), which was held during the 7th International Conference on Biomedical Engineering and Biotechnology in Nanjing, China. This source contains two databases: the data from CPSC 2018 (the CPSC Database) and unused data from CPSC 2018 (the CPSC-Extra Database). Together, these databases contain 13,256 ECGs (10,330 ECGs shared as training data, 1,463 retained as validation data, and 1,463 retained as test data). We shared the training set and an unused dataset from CPSC 2018 as training data, and we split the test set from CPSC 2018 into validation and test sets. Each recording is between 6 and 144 seconds long with a sampling frequency of 500 Hz. - The second source is the St Petersburg INCART 12-lead Arrhythmia Database. This source contains 74 annotated ECGs (all shared as training data) extracted from 32 Holter monitor recordings. Each recording is 30 minutes long with a sampling frequency of 257 Hz. - The third source is the Physikalisch-Technische Bundesanstalt (PTB) and includes two public datasets: the PTB and the PTB-XL databases. The source contains 22,353 ECGs (all shared as training data). Each recording is between 10 and 120 seconds long with a sampling frequency of either 500 or 1,000 Hz. - The fourth source is a Georgia database which represents a unique demographic of the Southeastern United States. This source contains 20,672 ECGs (10,344 ECGs shared as training data, 5,167 retained as validation data, and 5,161 retained as test data). Each recording is between 5 and 10 seconds long with a sampling frequency of 500 Hz. - The fifth source is an undisclosed American database that is geographically distinct from the Georgia database. This source contains 10,000 ECGs (all retained as test data). - The sixth source is the Chapman University, Shaoxing People’s Hospital (Chapman-Shaoxing) and Ningbo First Hospital (Ningbo) database. This source contains 45,152 ECGS (all shared as training data). Each recording is 10 seconds long with a sampling frequency of 500 Hz. - The seventh source is UMich Database from the University of Michigan. This source contains 19,642 ECGs (all retained as test data). Each recording is 10 seconds long with a sampling frequency of either 250 Hz or 500 Hz. Like other real-world datasets, different databases may have different proportions of cardiac abnormalities, but all of the labels in the validation or test data are represented in the training data. Moreover, while this is a curated dataset, some of the data and labels are likely to have errors, and an important part of the Challenge is to work out these issues. In particular, some of the databases have human-overread machine labels with single or multiple human readers, so the quality of the labels varies between databases. You can find more information about the label mappings of the Challenge training data in this table. The six-lead, four-lead, three-lead, and two-lead validation data are reduced-lead versions of the twelve-lead validation data: the same recordings with the same header data but only with signal data for the relevant leads. We are not planning to release the test data at any point, including after the end of the Challenge. Requests for the test data will not receive a response. We do not release test data to prevent overfitting on the test data and claims or publications of inflated performances. We will entertain requests to run code on the test data after the Challenge on a limited basis based on publication necessity and capacity. (The Challenge is largely staged by volunteers.) # Data Format All data was formatted in WFDB format. Each ECG recording uses a binary MATLAB v4 file (see page 27) for the ECG signal data and a plain text file in WFDB header format for the recording and patient attributes, including the diagnosis, i.e., the labels for the recording. The binary files can be read using the load function in MATLAB and the scipy.io.loadmat function in Python; see our MATLAB and Python example code for working examples. The first line of the header provides information about the total number of leads and the total number of samples or time points per lead, the following lines describe how each lead was encoded, and the last lines provide information on the demographics and diagnosis of the patient. For example, a header file A0001.hea may have the following contents: A0001 12 500 7500 05-Feb-2020 11:39:16 A0001.mat 16+24 1000/mV 16 0 28 -1716 0 I A0001.mat 16+24 1000/mV 16 0 7 2029 0 II A0001.mat 16+24 1000/mV 16 0 -21 3745 0 III A0001.mat 16+24 1000/mV 16 0 -17 3680 0 aVR A0001.mat 16+24 1000/mV 16 0 24 -2664 0 aVL A0001.mat 16+24 1000/mV 16 0 -7 -1499 0 aVF A0001.mat 16+24 1000/mV 16 0 -290 390 0 V1 A0001.mat 16+24 1000/mV 16 0 -204 157 0 V2 A0001.mat 16+24 1000/mV 16 0 -96 -2555 0 V3 A0001.mat 16+24 1000/mV 16 0 -112 49 0 V4 A0001.mat 16+24 1000/mV 16 0 -596 -321 0 V5 A0001.mat 16+24 1000/mV 16 0 -16 -3112 0 V6 #Age: 74 #Sex: Male #Dx: 426783006 #Rx: Unknown #Hx: Unknown #Sx: Unknown From the first line of the file, we see that the recording number is A0001, and the recording file is A0001.mat. The recording has 12 leads, each recorded at a 500 Hz sampling frequency, and contains 7500 samples. From the next 12 lines of the file (one for each lead), we see that each signal was written at 16 bits with an offset of 24 bits, the floating point number (analog-to-digital converter (ADC) units per physical unit) is 1000/mV, the resolution of the analog-to-digital converter (ADC) used to digitize the signal is 16 bits, and the baseline value corresponding to 0 physical units is 0. The first value of the signal (-1716, etc.), the checksum (0, etc.), and the lead name (I, etc.) are the last three entries of each of these lines. From the final 6 lines, we see that the patient is a 74-year-old male with a diagnosis (Dx) of 426783006, which is the SNOMED-CT code for sinus rhythm. The medical prescription (Rx), history (Hx), and symptom or surgery (Sx) are unknown. Please visit WFDB header format for more information on the header file and variables.

We have cleaned the noisy IMDB-WIKI dataset using a constrained clustering method, resulting this new benchmark for in-the-wild age estimation. The annotations also allow this dataset to use for some other tasks, like gender classification and face recognition/verification. For more details, please refer to our FPAge paper.

The LAGENDA dataset is a large-scale dataset with age and gender annotations for face and body bounding boxes. The dataset consists of 67,159 images from the Open Images Dataset and comprises 84,192 pairs (FaceCrop, BodyCrop). This dataset offers a high level of diversity, encompassing various scenes and domains. It contains minimal celebrity data, thus reflecting real-world, in-the-wild scenarios. The dataset spans a wide age range, from 0 to 95 years old.

At RSNA 2017 there was a contest to correctly identify the age of a child from an X-ray of their hand.

KANFace consists of 40K still images and 44K sequences (14.5M video frames in total) captured in unconstrained, real-world conditions from 1,045 subjects. The dataset is manually annotated in terms of identity, exact age, gender and kinship. Source: KANFace Dataset

EEG signals from 60 users have been recorded whose age range lies between 6 and 55 years. Among all, there were 25 females and 35 male users. In general, all the participants were either school children or belonged to the socioeconomic cross section of the population with no medical history. The EEG recordings were acquired from all 14 electrodes operating at a sampling rate of 128 Hz. During recording, the participants were asked to comfortably sit on the chair with clear thoughts and a relaxed state.

A large-scale, first-of-its-kind database aimed at generating a better understanding of the way children interact with mobile devices during their development process. ChildCIdbv1 comprises data collected from 438 children, from 18 months to 8 years old, encompassing the first three development stages of Piaget's theory. Data collected spans interaction with screens using both finger and pen stylus, information regarding the previous experience of the child with mobile devices, the child’s grade level, and whether attention-deficit/hyperactivity disorder (ADHD) is present. Use cases: Child age detection based on device interaction.

Facial Skeletal Angles (Glabella and Maxilla Angle and Length and Width of Piriformis)

A high-resolution version of VGGFace2 for academic face editing purposes. This project uses GFPGAN for image restoration and insightface for data preprocessing (crop and align).