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.

Custom WiderFace Dataset

## Overview

Custom WiderFace Dataset is a dataset for object detection tasks - it contains Face annotations for 1,644 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).

Sourced from: https://www.tensorflow.org/datasets/catalog/wider_face

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.

Homepage: http://shuoyang1213.me/WIDERFACE/

Source code: tfds.object_detection.WiderFace

Versions:

0.1.0 (default): No release notes. Download size: 3.42 GiB

Dataset size: 3.45 GiB

Auto-cached (documentation): No

Splits:

Widerface

## Overview

Widerface is a dataset for object detection tasks - it contains Face annotations for 1,076 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).

This is dataset for face detection with WIDERFACE for train and test, FDDB for test face and LFPW for test landmark

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).

Work Done

Dataset prepared proper structure with ".txt label files" for YOLO12.

Dataset Description

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.

Original Dataset from: http://shuoyang1213.me/WIDE

License

Creative Common License (cc by-nc-nd)

Install TAO Toolkit using pip and make sure to pull its docker container with GPU runtime (if using Colab or similar service) otherwise, the operation will fail. It is a relatively large image (21GB) as a result it will a while to download. Training YoloV4 Tiny with widerface dataset using Nvidia TAO toolkit. TAO Yolov4 Tiny requires the input image shape to be a multiple of 32 therefore, the images were resized to 768 x 768 and were also converted to PNG format. Could not find the pretrained… See the full description on the dataset page: https://huggingface.co/datasets/tahirishaq10/widerface_kitti.

Dataset

This dataset was created by Meet Singh

Contents

WIDERFACE (PA)

## Overview

WIDERFACE (PA) is a dataset for object detection tasks - it contains Privacy_awareness annotations for 275 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).

license: mit language: en tags:

computer-vision face-detection image-classification

  Cropped Faces from WIDER FACE Dataset





  Dataset Description

This repository provides two key datasets for face-related computer vision tasks, delivered as two separate .zip archives:

WIDER_val.zip: A compressed archive of the original validation set from the well-known WIDER FACE dataset. It contains 3,226 images with a wide variety of scales, poses, and occlusions.… See the full description on the dataset page: https://huggingface.co/datasets/amannagrawall002/croppedFaceDataset.

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).

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 Duc Hoa

Contents

Dataset

This dataset was created by HoangKim_14

Contents

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).

Given a blurred image, image deblurring aims to produce a clear, high-quality image that accurately represents the original scene. Blurring can be caused by various factors such as camera shake, fast motion, out-of-focus objects, etc. making it a particularly challenging computer vision problem. This has led to the recent development of a large spectrum of deblurring models and unique datasets.

Despite the rapid advancement in image deblurring, the process of finding and pre-processing a number of datasets for training and testing purposes has been both time exhaustive and unnecessarily complicated for both experts and non-experts alike. Moreover, there is a serious lack of ready-to-use domain-specific datasets such as face and text deblurring datasets.

To this end, the following card contains a curated list of ready-to-use image deblurring datasets for training and testing various deblurring models. Additionally, we have created an extensive, highly customizable python package for single image deblurring called DBlur that can be used to train and test various SOTA models on the given datasets just with 2-3 lines of code.

Following is a list of the datasets that are currently provided: - GoPro: The GoPro dataset for deblurring consists of 3,214 blurred images with a size of 1,280×720 that are divided into 2,103 training images and 1,111 test images. - HIDE: HIDE is a motion-blurred dataset that includes 2025 blurred images for testing. It mainly focus on pedestrians and street scenes. - RealBlur: The RealBlur testing dataset consists of two subsets. The first is RealBlur-J, consisting of 1900 camera JPEG outputs. The second is RealBlur-R, consisting of 1900 RAW images. The RAW images are generated by using white balance, demosaicking, and denoising operations. - CelebA: A face deblurring dataset created using the CelebA dataset which consists of 2 000 000 training images, 1299 validation images, and 1300 testing images. The blurred images were created using the blurred kernels provided by Shent et al. 2018 - Helen: A face deblurring dataset created using the Helen dataset which consists of 2 000 training images, 155 validation images, and 155 testing images. The blurred images were created using the blurred kernels provided by Shent et al. 2018 - Wider-Face: A face deblurring dataset created using the Wider-Face dataset which consists of 4080 training images, 567 validation images, and 567 testing images. The blurred images were created using the blurred kernels provided by Shent et al. 2018
- TextOCR: A text deblurring dataset created using the TextOCR dataset which consists of 5000 training images, 500 validation images, and 500 testing images. The blurred images were created using the blurred kernels provided by Shent et al. 2018