Omkar2118/datasets huggingface dataset:
RUGD
Full name: RUGD: Robot Unstructured Ground Driving

A Video Dataset for Visual Perception and Autonomous Navigation in Unstructured Environments. 
Website: http://rugd.vision/

The RUGD dataset focuses on semantic understanding of unstructured outdoor environments for applications in off-road autonomous navigation. The datset is comprised of video sequences captured from the camera onboard a mobile robot platform. The overall goal of the data collection is to provide a more representative dataset of environments that lack structural cues that are commonly found in urban city autonmous navigation datasets. The platform used for data collection is small enough to manuever in cluttered environments, and is rugged enough to traverse through challenging terrain to explore more unstructured areas of an environment.

Dense pixel-wise annotations are provided for every fifth frame in a video sequence. The ontology is defined to support fine-grained terrain identification for path planning tasks, and object identification to avoid obstacles and localize landmarks. In total, 24 semantic categories can be found in the annotations of the videos including eight unique terrain types.
Drone-Action
Full name: Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition

Website: https://asankagp.github.io/droneaction/
RoCoG-v2
Full name: Robot Control Gestures

RoCoG-v2 (Robot Control Gestures) is a dataset intended to support the study of synthetic-to-real and ground-to-air video domain adaptation. It contains over 100K synthetically-generated videos of human avatars performing gestures from seven (7) classes. It also provides videos of real humans performing the same gestures from both ground and air perspectives

Introducing paper: Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances
UTCD
Full name: Universal Text Classification Dataset

UTCD is a compilation of 18 classification datasets spanning 3 categories of Sentiment, Intent/Dialogue, and Topic classification. UTCD focuses on the task of zero-shot text classification where the candidate labels are descriptive of the text being classified. UTCD consists of ~ 6M/800K train/test examples.

Introducing paper: Label Agnostic Pre-training for Zero-shot Text Classification
InspiRe
Full name: Inspiring and non-inspiring posts from Reddit

We analyze **social media** posts to tease out what makes a post inspiring and what topics are inspiring. We release a dataset of **5,800 inspiring and 5,800 non-inspiring English-language** public post unique ids collected from a dump of **Reddit public posts** made available by a third party and use linguistic heuristics to automatically detect which social media English-language posts are inspiring.

Introducing paper: Detecting Inspiring Content on Social Media
WhenAct
Full name: Temporal Human Action Localization in Lifestyle Vlogs

We consider the task of **temporal human action localization in lifestyle vlogs**. We introduce a novel dataset consisting of **manual annotations** of **temporal localization for 13,000 narrated actions in 1,200 video clips**.
We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their **expected duration**. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods and leads to improvements over previous work for the task of temporal action localization.

Introducing paper: When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs