Omkar2118/datasets huggingface dataset:
Pretrained Models of the Benchmarking Algorithms for UVCGAN

The pretrained models from four image translation algorithms: ACL-GAN, Council-GAN, CycleGAN, and U-GAT-IT on three benchmarking datasets: Selfie2Anime, CelebA_gender, CelebA_glasses.

We trained the models to provide benchmarks for the algorithm we detailed in the paper "UVCGAN: UNet Vision Transformer Cycle-consistent GAN for Unpaired Image-to-Image Translation.". 

We only trained a model if a pretrained model is provided by a benchmarking algorithm.

Introducing paper: UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Fitness-AQA
Full name: Fitness Action Quality Assessment [ECCV 2022]

Largest, first-of-its-kind, in-the-wild, fine-grained workout/exercise posture analysis dataset, covering three different exercises: BackSquat, Barbell Row, and Overhead Press. Seven different types of exercise errors are covered. Unlabeled data is also provided to facilitate self-supervised learning.

Introducing paper: Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
WITS
Full name: Why Is This Sarcastic?

This dataset is an extension of MASAC, a multimodal, multi-party, Hindi-English code-mixed dialogue dataset compiled from the popular Indian TV show,
‘Sarabhai v/s Sarabhai’.  WITS was created by augmenting MASAC with natural language explanations for each sarcastic dialogue. The dataset consists of the transcribed sarcastic dialogues from 55 episodes of the TV show, along with audio and video multimodal signals. It was designed to facilitate Sarcasm Explanation in Dialogue (SED), a novel task aimed at generating a natural language explanation for a given sarcastic dialogue, that spells out the intended irony. Each data instance in WITS is associated with a corresponding video, audio, and textual transcript where the last utterance is sarcastic in nature. All the final selected explanations contain the following attributes:

• Sarcasm source: The speaker in the dialog who is being sarcastic.
• Sarcasm target: The person/ thing towards whom the sarcasm is directed.
• Action word: Verb/ action used to describe how the sarcasm is taking place.  e.g. mocking, insults, taunts, etc.
• Description: A description of the scene to help contextualize the sarcasm.

Introducing paper: When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues
FloW

- Marine wastes are severely threatening marine animals and their habitat, also causing an impact on human life through toxic substances transportation and accumulation. To prevent the wastes especially the plastic trash from getting into the ocean, it is essential to detect and clean the floating wastes in inland waters efficiently like in rivers, lakes, and canals.

- FloW is the first dataset for floating waste detection in inland waters. It contains a vision-based sub-dataset, FloW-Img, and a multimodal dataset, FloW-RI which contains the spatial and temporal calibrated image and millimeter-wave radar data.

- By publishing Flow, it is hoped that more attention from research communities could be paid to floating waste detection in inland waters as well as the challenging small object detection over the water surface. In addition, waste detection based on millimeter-wave radar data or the fusion of image and radar data is also a novel task and FloW provides accessible real-world data.
ATOM3D

**ATOM3D** is a unified collection of datasets concerning the three-dimensional structure of biomolecules, including proteins, small molecules, and nucleic acids. These datasets are specifically designed to provide a benchmark for machine learning methods which operate on 3D molecular structure, and represent a variety of important structural, functional, and engineering tasks. All datasets are provided in a standardized format along with a Python package containing processing code, utilities, models, and dataloaders for common machine learning frameworks such as PyTorch. ATOM3D is designed to be a living database, where datasets are updated and tasks are added as the field progresses.

Description from: [https://www.atom3d.ai/](https://www.atom3d.ai/)

Introducing paper: ATOM3D: Tasks On Molecules in Three Dimensions