Omkar2118/datasets huggingface dataset:
IDDA

**IDDA** is a large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains. The dataset has been created to explicitly address the challenges of domain shift between training and test data in various weather and view point conditions, in seven different city types.

Introducing paper: IDDA: a large-scale multi-domain dataset for autonomous driving
SyRIP

**SyRIP** is a hybrid synthetic and real infant pose (SyRIP) dataset with small yet diverse real infant images as well as generated synthetic infant poses and (2) a multi-stage invariant representation learning strategy that could transfer the knowledge from the adjacent domains of adult poses and synthetic infant images into our fine-tuned domain-adapted infant pose

Introducing paper: Invariant Representation Learning for Infant Pose Estimation with Small Data
RobustBench

**RobustBench** is a benchmark of adversarial robustness, which as accurately as possible reflects the robustness of the considered models within a reasonable computational budget. To this end, we start by considering the image classification task and introduce restrictions (possibly loosened in the future) on the allowed models.

Introducing paper: RobustBench: a standardized adversarial robustness benchmark
WaveFake

WaveFake is a dataset for audio deepfake detection. The dataset consists of a large-scale dataset of over 100K generated audio clips.

Introducing paper: WaveFake: A Data Set to Facilitate Audio Deepfake Detection