Omkar2118/datasets huggingface dataset:
DigiFace-1M

**DigiFace-1M** is a **synthetic dataset** for face recognition, obtained by rendering digital faces using a computer graphics pipeline. It contains **1.22M images** of **110K unique identities**. The dataset consists of two parts. The **first part** contains **720K images** with **10K identities**. For each identity, 4 different sets of accessories are sampled and 18 images are rendered for each set. The **second part** contains **500K images** with **100K identities**. For each identity, only one set of accessories is sampled and only 5 images are rendered. Following the format of the existing datasets, we provide the aligned crop around the face, resized into $112 \times 112$ resolution.

Please visit the website for more detail.

Introducing paper: DigiFace-1M: 1 Million Digital Face Images for Face Recognition
HR-ShanghaiTech

The human-Related version of the ShanghaiTech Campus, was first presented by Morais et al. in the paper "Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos".

Introducing paper: Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
AesVQA

AesVQA is a dataset that contains 72168 high-quality images and 324756 pairs of aesthetic questions. This dataset addresses the task of aesthetic VQA and introduces subjectiveness into VQA tasks.

Source: [Aesthetic Visual Question Answering of Photographs](https://arxiv.org/pdf/2208.05798v1.pdf)

Image Source: [https://arxiv.org/pdf/2208.05798v1.pdf](https://arxiv.org/pdf/2208.05798v1.pdf)

Introducing paper: Aesthetic Visual Question Answering of Photographs
NSVA
Full name: NBA dataset for Sports Video Analysis

NVSA is a large-scale NBA dataset for Sports Video Analysis (NSVA) with a focus on sports video captioning. This dataset consists of more than 32K video clips and it is also designed to address two additional tasks, namely fine-grained sports action recognition and salient player identification.

Introducing paper: Sports Video Analysis on Large-Scale Data
ChiQA
Full name: Chinese VQA

ChiQA is a dataset designed for visual question answering tasks that not only measures the relatedness but also measures the answerability, which demands more fine-grained vision and language reasoning. It contains more than 40K questions and more than 200K question-images pairs. The questions are real-world image-independent queries that are more various and unbiased.

Introducing paper: ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding
VQA-VS
Full name: a new VQA benchmark considering Varying Shortcuts

The  current OOD benchmark VQA-CP v2 only considers one type of shortcut (from question type to answer) and thus still cannot guarantee that the modelrelies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation,  VQA-VS proposes a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, VQA-VS overcomes three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. VQA-VS provides a more rigorous and comprehensive testbed for shortcut learning in VQA.

Introducing paper: Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
MUAD
Full name: Multiple Uncertainties for Autonomous Driving

The MUAD dataset (Multiple Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation, depth estimation, object, and instance detection. Predictive uncertainty estimation is essential for the safe deployment of Deep Neural Networks in real-world autonomous systems and MUAD allows to a better assess the impact of different sources of uncertainty on model performance.

Introducing paper: MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks