Omkar2118/datasets huggingface dataset:
WISDOM
Full name: Warehouse Instance Segmentation Dataset for Object Manipulation

Synthetic training dataset of 50,000 depth images and 320,000 object masks using simulated heaps of 3D CAD models. 

Source: [Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data](/paper/segmenting-unknown-3d-objects-from-real-depth)

Introducing paper: Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
WLASL
Full name: Word-Level American Sign Language

**WLASL** is a large video dataset for **Word-Level American Sign Language** (ASL) recognition, which features 2,000 common different words in ASL.

Source: [https://github.com/dxli94/WLASL](https://github.com/dxli94/WLASL)

Introducing paper: Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
WLD
Full name: WildLife Documentary

**WildLife Documentary** is an animal object detection dataset. It contains 15 documentary films that are downloaded from YouTube. The videos vary between 9 minutes to as long as 50 minutes, with resolution ranging from 360p
to 1080p. A unique property of this dataset is that all videos are accompanied with subtitles that are automatically generated from speech by YouTube. The subtitles are revised manually to correct obvious spelling mistakes. All the animals in the videos are annotated, resulting in more than 4098 object tracklets of 60 different visual
concepts, e.g., ‘tiger’, ‘koala’, ‘langur’, and ‘ostrich’.

Introducing paper: Discover and Learn New Objects from Documentaries
WNLaMPro
Full name: WordNet Language Model Probing

The **WordNet Language Model Probing** (**WNLaMPro**) dataset consists of relations between keywords and words. It contains 4 different kinds of relations: Antonym, Hypernym, Cohyponym and Corruption.

Source: [https://arxiv.org/pdf/1904.06707.pdf](https://arxiv.org/pdf/1904.06707.pdf)

Introducing paper: Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking
Workplace Sexual Harassment

The goal of this dataset is to understand how people experience sexism and sexual harassment in the workplace by discovering themes in 2,362 experiences posted on the Everyday Sexism Project's website

Source: [https://arxiv.org/abs/1907.00510](https://arxiv.org/abs/1907.00510)

Introducing paper: Hidden in Plain Sight For Too Long: Using Text Mining Techniques to Shine a Light on Workplace Sexism and Sexual Harassment