Omkar2118/datasets huggingface dataset:
VidHOI

VidHOI is a video-based human-object interaction detection benchmark. VidHOI is based on VidOR which is densely annotated with all humans and predefined objects showing up in each frame. VidOR is also more challenging as the videos are non-volunteering user-generated and thus jittery at times.

Image source: [https://xdshang.github.io/docs/vidor.html](https://xdshang.github.io/docs/vidor.html)

Introducing paper: ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos
Dataset for: "It is just a flu: Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations"

The dataset consists of three files: the metadata, comments, and captions of the ground-truth dataset videos collected and manually reviewed in this paper.

1. Video Metadata:
- "groundtruth_videos.json": Contains the metadata of our manually reviewed ground-truth dataset videos. The ground-truth dataset includes 1,197 science, 1,325 pseudoscience, and 3,212 irrelevant videos. More specifically, it includes the metadata of videos related to the following pseudoscientific topics:
  - COVID-19: (607 science, 368 pseudoscience, 721 irrelevant videos)
  - Anti-vaccination (363 science, 394 pseudoscience, and 1,060 irrelevant videos)
  - Anti-mask (65 science, 188 pseudoscience, and 724 irrelevant videos)
  - Flat Earth (162 science, 375 pseudoscience, and 707 irrelevant videos)

Note, that 600 of the videos in this dataset include the "annotation.manual_review_label" attribute, which is the label assigned by the first author of this paper to evaluate the performance of the crowdsourced annotation process.

- Video Metadata Description:
  - "search_term": The search terms used to search YouTube and retrieve these videos during our data collection. It can be one of the following search terms: 'covid-19', 'coronavirus', 'anti-vaccination', 'anti-vaxx', 'anti-mask', or 'flat earth'.
  - "annotation.annotations": The list of the three annotations assigned to each video by our crowdsourced annotators.
  - "annotation.label": The annotation label assigned to the video based on the majority agreement of the crowdsourced annotators.
  - "annotation.manual_review_label": The label assigned by the first author of this paper to evaluate the performance of the crowdsourced annotation process.
  - "isSeed": 0 if the video is a seed video of our data collection, 1 if it is a recommended video of a seed video.
  - "relatedVideos": The recommended videos of the given video as returned by the YouTube Data API.

2. Video Comments: 
- "groundtruth_videos_comments_ids.json": Includes the identifiers of the comments of our ground-truth videos.

3. Video Transcripts:
- "groundtruth_videos_transcripts.json": Includes the captions of our ground-truth videos.
If you use this dataset in any publication, of any form and kind, please cite using this data.

Introducing paper: "It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations
Enron Emails

This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation.
MacaquePose
Full name: MacaquePose

**MacaquePose** is an animal pose estimation dataset containing pictures of macaque monkeys and manually labeled annotations on them.
Vinegar Fly
Full name: Vinegar Fly

**Vinegar Fly** is a pose estimation dataset for fruit flies.
Desert Locust
Full name: Desert Locust

**Desert Locus** is a animal pose estimation dataset for desert locuses.
Grévy’s Zebra

**Grévy’s Zebra** is an animal pose estimation dataset for zebras.
RHD
Full name: Rendered Hand Pose

**Rendered Hand Pose (RHD)** is a dataset for hand pose estimation. It provides segmentation maps with 33 classes: three for each finger, palm, person, and background. The 3D kinematic model of the hand provides 21 keypoints per hand: 4 keypoints per finger and one keypoint close to the wrist.

Introducing paper: Learning to Estimate 3D Hand Pose from Single RGB Images