데이터셋 탐색

WIDER is a dataset for complex event recognition from static images. As of v0.1, it contains 61 event categories and around 50574 images annotated with event class labels.

The Wider Facial Landmarks in the Wild or WFLW database contains 10000 faces (7500 for training and 2500 for testing) with 98 annotated landmarks. This database also features rich attribute annotations in terms of occlusion, head pose, make-up, illumination, blur and expressions. Source: Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion Image Source: https://wywu.github.io/projects/LAB/WFLW.html

WiderPerson contains a total of 13,382 images with 399,786 annotations, i.e., 29.87 annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions. Hence, pedestrians in the proposed dataset are extremely challenging due to large variations in the scenario and occlusion, which is suitable to evaluate pedestrian detectors in the wild. Source: WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild Image Source: http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson/

The WIDER Attribute dataset is a human attribute recognition dataset with human attribute and image event annotations. Images are selected from the WIDER dataset. There are a total of 13,789 images. A bounding box is annotated for each person in these images, with no more than 20 people (with top resolutions) in a crowd image, resulting in 57,524 boxes in total and 4+ boxes per image on average. For each bounding box, 14 distinct human attributes are labelled. There are 805,336 labels in total.

This Wider-Test-200 dataset is introduced in the following paper: "Towards Unsupervised Blind Face Restoration using Diffusion Prior" Please visit our website and refer to our paper for more information on the dataset and our method: https://dt-bfr.github.io/