WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('wider_face', 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/wider_face-0.1.0.png" alt="Visualization" width="500px">

Sample Wider Face

## Overview

Sample Wider Face is a dataset for object detection tasks - it contains Face annotations for 10,000 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).

WIDER FACE: A face detection benchmark.

WIDER FACE: A face detection benchmark dataset.

WIDER FACE With Faces Over 100px

## Overview

WIDER FACE With Faces Over 100px is a dataset for object detection tasks - it contains Faces annotations for 3,371 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).

Face detection dataset for unconstrained settings, including WIDER FACE and FDDB datasets.

WIDER FACE 3000

## Overview

WIDER FACE 3000 is a dataset for object detection tasks - it contains Face annotations for 3,000 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).

Dataset

This dataset was created by AkuMasihPemula

Released under MIT

Contents

Dataset

This dataset was created by Duc Hoa

Contents

WIDER_FACE_VAL

## Overview

WIDER_FACE_VAL is a dataset for object detection tasks - it contains Faces annotations for 3,226 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).

WIDER_FACE_TRAIN_2

## Overview

WIDER_FACE_TRAIN_2 is a dataset for object detection tasks - it contains Faces annotations for 6,038 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

WIDER_FACE_TRAIN_1

## Overview

WIDER_FACE_TRAIN_1 is a dataset for object detection tasks - it contains Faces annotations for 6,841 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).

YOLOv8 Detection Model

  Datasets





  Face

Anime Face CreateML xml2txt AN wider face

  Hand

AnHDet hand-detection-fuao9

  Person

coco2017 (only person) AniSeg skytnt/anime-segmentation

  deepfashion2

deepfashion2

id label

0 short_sleeved_shirt

1 long_sleeved_shirt

2 short_sleeved_outwear

3 long_sleeved_outwear

4 vest

5 sling

6 shorts

7 trousers

8 skirt

9 short_sleeved_dress

10 long_sleeved_dress

11… See the full description on the dataset page: https://huggingface.co/datasets/Blankse/SegsmakerAdetailer.

Live Face Anti-Spoof Dataset

A live face dataset is crucial for advancing computer vision tasks such as face detection, anti-spoofing detection, and face recognition. The Live Face Anti-Spoof Dataset offered by Ainnotate is specifically designed to train algorithms for anti-spoofing purposes, ensuring that AI systems can accurately differentiate between real and fake faces in various scenarios.

Key Features:

Comprehensive Video Collection: The dataset features thousands of videos showcasing a diverse range of individuals, including males and females, with and without glasses. It also includes men with beards, mustaches, and clean-shaven faces. Lighting Conditions: Videos are captured in both indoor and outdoor environments, ensuring that the data covers a wide range of lighting conditions, making it highly applicable for real-world use. Data Collection Method: Our datasets are gathered through a community-driven approach, leveraging our extensive network of over 700k users across various Telegram apps. This method ensures that the data is not only diverse but also ethically sourced with full consent from participants, providing reliable and real-world applicable data for training AI models. Versatility: This dataset is ideal for training models in face detection, anti-spoofing, and face recognition tasks, offering robust support for these essential computer vision applications. In addition to the Live Face Anti-Spoof Dataset, FileMarket provides specialized datasets across various categories to support a wide range of AI and machine learning projects:

Object Detection Data: Perfect for training AI in image and video analysis. Machine Learning (ML) Data: Offers a broad spectrum of applications, from predictive analytics to natural language processing (NLP). Large Language Model (LLM) Data: Designed to support text generation, chatbots, and machine translation models. Deep Learning (DL) Data: Essential for developing complex neural networks and deep learning models. Biometric Data: Includes diverse datasets for facial recognition, fingerprint analysis, and other biometric applications. This live face dataset, alongside our other specialized data categories, empowers your AI projects by providing high-quality, diverse, and comprehensive datasets. Whether your focus is on anti-spoofing detection, face recognition, or other biometric and machine learning tasks, our data offerings are tailored to meet your specific needs.

While there are a number of datasets of surgical masks out there, the reality is that a multitude of face coverings are used in the real world. The UK Department of Health and Social Care define a proper facecovering as “…something which safely covers the nose and mouth, and securely fit[s] round the side of the face”

This dataset aims to tackle this by including a multitude of face coverings, from scarfs and shirts to surgical masks. This dataset contains 9106 images of covered and uncovered faces both in the wild and closeup. Most of the images have been collected from MAFA and WIDER FACE, however all faces in the set have been manually checked and relabelled to fit the problem. Each image is accompanied by a txt file with yolo-format bounding boxes. There are two classes in this dataset: "with covering" and "without covering".

Face Detection Round 2

## Overview

Face Detection Round 2 is a dataset for object detection tasks - it contains Face annotations for 1,010 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).

Blind face restoration dataset

UltraVideo: High-Quality UHD 4K Video Dataset

🤓 Project  | 📑 Paper  | 🤗 Hugging Face (UltraVideo Dataset))  | 🤗 Hugging Face (UltraVideo-Long Dataset))  | 🤗 Hugging Face (UltraWan-1K/4K Weights) 

UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions

🎋 Click below image to watch the 4K demo video. 🤓 First open-sourced UHD-4K/8K video datasets with comprehensive structured (10 types) captions.🤓 Native 1K/4K videos generation by UltraWan.… See the full description on the dataset page: https://huggingface.co/datasets/APRIL-AIGC/UltraVideo-Long.

This dataset provides images of faces and raw files of sculpture from the wider Mediterranean region, including Iraq, Iran, Egypt, Levant, Greece, Anatolia, Cyprus, and elsewhere.

People are poorer at recognising other race faces, referred to as an own race bias (ORB). This project examined perceptual tasks requiring attention to the identity of faces and access to long-term memory (face matching and detection). A sequential matching task involving own or other race faces showed ORB only on same and not different decisions.

    Examining the causes of ORB in relation to configural and featural processing by manipulating the orientation of stimuli, showed that inverting faces increased response latencies, with increase being greater for own race than other race faces. Using whole or part faces in a simultaneous matching task to compare local versus global configural processing, showed that face context is important for own race but not other race faces, suggesting the difference in processing may relate to local configural processing. Face detection viewing Caucasian or Asian faces or non-face stimuli, showed no overall difference in face detection latencies for the two races, while obscuring features of a face affected response latencies to own race but not other race faces. ORB thus influences several tasks not requiring access to long-term memory, showing that previous accounts of ORB based only on access to memorial representations need extending.