Omkar2118/datasets huggingface dataset:
LFWA

LFWA is a popular unconstrained facial attribute dataset, which consists of 13,143 facial images of 5,749 identities. Each facial image has 40 attribute annotations.

Image Source: [https://arxiv.org/pdf/1604.07360.pdf](https://arxiv.org/pdf/1604.07360.pdf)

Introducing paper: Deep Learning Face Attributes in the Wild
Clothing Attributes Dataset

We introduce the Clothing Attribute Dataset for promoting research in learning visual attributes for objects. The dataset contains 1856 images, with 26 ground truth clothing attributes such as "long-sleeves", "has collar", and "striped pattern". The labels were collected using Amazon Mechanical Turk.

Source: [Describing Clothing by Semantic Attributes](https://huizhongchen.github.io/papers/ECCV2012_ClothingAttributes.pdf)
Image Source: [https://huizhongchen.github.io/papers/ECCV2012_ClothingAttributes.pdf](https://huizhongchen.github.io/papers/ECCV2012_ClothingAttributes.pdf)
Image Aesthetics dataset

The image aesthetic benchmark [18] consists of 10800
Flickr photos of four categories, i.e., “animals”, “urban”,
“people” and “nature”, and is constructed originally to retrieve beautiful yet unpopular images in social networks.
The ground truths of the photos in the benchmark are five
aesthetic grades: “Unacceptable” - images with extremely
low quality, out of focus or underexposed, “Flawed” - images with some technical flaws and without any artistic
value, “Ordinary” - standard quality images without technical flaws, “Professional” - professional-quality images
with some artistic value, and “Exceptional” - very appealing images showing both outstanding professional quality
and high artistic value. 

Image Source: [OrdinalCLIP](https://arxiv.org/pdf/2206.02338.pdf)

Introducing paper: An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
Historical Color Image Dataset

The historical color image dataset is collected for the task of automatically estimating the age of
historical color photos. Each image is annotated with its associated decade, where five decades from the 1930s to 1970s are considered. There are 265 images for each category

Image Source: [Dating Historical Color Images](https://link.springer.com/chapter/10.1007/978-3-642-33783-3_36)

Introducing paper: Dating historical color images
KinFaceW-I

KinFaceW-I dataset contains 533
pairs of facial images of persons with a kin relation. Four
different kin relations are considered in the dataset: father
and daughter (F-D) with 134 pairs, father and son (F-S) with
156 pairs, mother and daughter (M-D) with 127 pairs, mother and son (M-S) with 116 pairs. Each sample is composed
of one parent face image and one child face image.

Image Source: [Neighborhood Repulsed Metric Learning for Kinship Verification](https://ieeexplore.ieee.org/document/6562692)

Introducing paper: Neighborhood Repulsed Metric Learning for Kinship Verification
KinFaceW-II

KinFaceW-II Dataset consists of
1000 pairs of facial images of individuals with a kin relation. This database also considers four common kin relations: father and daughter (F-D), father and son (F-S),
mother and daughter (M-D), mother and son (M-S). Different from the KinFaceW-I database, the positive pairs in this
dataset are taken from the same photo.

Image Source: [Neighborhood Repulsed Metric Learning for Kinship Verification](https://ieeexplore.ieee.org/document/6562692)

Introducing paper: Neighborhood Repulsed Metric Learning for Kinship Verification
PIPA
Full name: People in Photo Album

The PIPA database is collected from Flickr photo albums for the task of person recognition. Then the dataset is extended with 26,915 person pair annotations based on the social domain theory.

Image Source: [A Domain Based Approach to Social Relation Recognition](https://arxiv.org/pdf/1704.06456.pdf)

Introducing paper: A Domain Based Approach to Social Relation Recognition
AUT-VI
Full name: Amirkabir campus dataset

AUT-VI is a super-challenging visual inertial dataset with 126 diverse sequences in 17 locations. This dataset contains dynamic objects, challenging loop-closure/map-reuse, different lighting conditions, reflections, and sudden camera movements to cover all extreme navigation scenarios. Moreover, the Android application for data capture is released to the public to support ongoing development efforts. 
This dataset aims to exploit the remaining challenges in VIO algorithms, in the hope of improving them to facilitate navigation for visually impaired individuals in both indoor and outdoor settings.

Introducing paper: Amirkabir campus dataset: Real-world challenges and scenarios of Visual Inertial Odometry (VIO) for visually impaired people
NIAID TB Portals (NIH)

X-rays, CT Images and Genomic Sequences representing cases of tuberculosis.
FMB Dataset
Full name: Full-time Multi-modality Benchmark Dataset

FMB contains 1500 well-registered infrared and visible image pairs with 14 annotated pixel-level categories. Also, it covers a wide range of pixel variations and various severe environments, e.g., dense fog, heavy rain, and low-light condition. The FMB dataset includes rich scenes under different illumination conditions, so that it enables fusion/segmentation model to improve the generalization ability greatly. We labeled 98.16% of all pixels into 14 different categories including Road, Sidewalk, Building, Traffic Light, Traffic Sign, Vegetation, Sky, Person, Car, Truck, Bus, Motorcycle, Bicycle and Pole, which often appear in real world automatic driving and semantic understanding tasks.

Introducing paper: Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
HaN-Seg
Full name: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge

Cancer in the region of the head and neck (HaN) is one of the most prominent cancers, for which radiotherapy represents an important treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional spatial description, i.e. segmentation, of the target volumes as well as OARs is required for optimal radiation dose distribution calculation, which is primarily performed using computed tomography (CT) images. However, the HaN region contains many OARs that are poorly visible in CT, but better visible in magnetic resonance (MR) images. Although attempts have been made towards the segmentation of OARs from MR images, so far there has been no evaluation of the impact the combined analysis of CT and MR images has on the segmentation of OARs in the HaN region. The Head and Neck Organ-at-Risk Multi-Modal Segmentation Challenge aims to promote the development of new and application of existing fully automated techniques for OAR segmentation in the HaN region from CT images that exploit the information of multiple imaging modalities so as to improve the accuracy of segmentation results.

Introducing paper: HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset
GeneCIS

GeneCIS benchmark is designed for measuring models’ ability to adapt to a range of similarity conditions, which is zero-shot evaluation only.

Introducing paper: GeneCIS: A Benchmark for General Conditional Image Similarity