Flickr-Faces-HQ (FFHQ) consists of 70,000 high-quality PNG images at 1024×1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled from Flickr, thus inheriting all the biases of that website, and automatically aligned and cropped using dlib. Only images under permissive licenses were collected. Various automatic filters were used to prune the set, and finally Amazon Mechanical Turk was used to remove the occasional statues, paintings, or photos of photos. Source: Flickr-Faces-HQ Dataset (FFHQ) Image Source: https://github.com/NVlabs/ffhq-dataset
VGGFace2 is a large-scale face recognition dataset. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession. VGGFace2 contains images from identities spanning a wide range of different ethnicities, accents, professions and ages. All face images are captured "in the wild", with pose and emotion variations and different lighting and occlusion conditions. Face distribution for different identities is varied, from 87 to 843, with an average of 362 images for each subject.
During the covid-19 era wearing face masks posed new challenges to face-related tasks, including facial recognition, face inpainting, expression recognition, and object removal. Mask region segmentation is a preliminary stage to tackle the occlusion issue corresponding to the face-related tasks. Existing masked face datasets are not procedure binary segmentation maps because Segmenting mask regions manually is a time-consuming operation. As a result, existing unmasking methods; synthesize training data by overlaying masks on existing face datasets. However, since these techniques rely on an artificially generated mask, their effects tend to seem unnatural. To address this issue, the masked face segmentation dataset(MFSD) provides the first public training dataset for the mask segmentation task.
Dataset for the DREAMING - Diminished Reality for Emerging Applications in Medicine through Inpainting Challenge! The DREAMING Challenge is part of ISBI 2024. Participate for Awards, Money Prices, Proceedings Paper, Top-Tier Challenge Summary Paper! More information about the challenge and registration can be found on the challenge platform! The DREAMING training dataset contains 100 diverse, photorealistic surgery scenes with unique patients, surroundings and obstructions from dynamically moving and static surgical instruments and hands holding them. We also provide the masks defining the area to be inpainted for each image.