Omkar2118/datasets huggingface dataset:
MOD20

**MOD20** is an action recognition dataset consisting of videos collected from YouTube and our own drone. The dataset contains 2,324 videos lasting a total of 240 minutes. The actions were selected from challenging and complex scenarios, and cover multiple viewpoints, from ground-level to bird's-eye view. The substantial variation in body size, number of people, viewpoints, camera motion, and background makes this dataset challenging for action recognition. The action classes, 720×720 size un-distorted clips and multi-viewpoint video selection extend the dataset's applicability to a wider research community.

Introducing paper: A Multi-viewpoint Outdoor Dataset for Human Action Recognition
NGAFID-MC

NGNGAFID-MC consists of over 7500 labeled flights, representing over 11,500 hours of per second flight data recorder readings of 23 sensor parameters.

Introducing paper: Predictive Maintenance for General Aviation Using Convolutional Transformers
FOD-A

FOD in Airports (FOD-A) is an image dataset of FOD, Foreign Object Degris, which consists of 31 object categories and over 30,000 annotation instances. The object categories have been selected based on guidance from prior documentation and related research by the Federal Aviation Administration (FAA).

Introducing paper: FOD-A: A Dataset for Foreign Object Debris in Airports
V-HICO

**V-HICO** is a dataset for human-object interaction in videos. There are 6,594 videos, including 5,297 training videos, 635 validation videos, 608 test videos, and 54 unseen test videos, of human-object interaction. To test the performance of models on common human-object interaction classes and generalization to new human-object interaction classes, we provide two test splits, the first one has the same human-object interaction classes in the training split while the second one consists of unseen novel classes.

V-HICO consists of 244 object classes and 99 action classes. There are 756 action-object pairwise classes in total. The unseen test dataset contains 51 object classes and 32 action classes with 52 action-object pairwise classes. All videos are labeled with text annotations of the human action and the associated object. The test and unseen dataset contain the annotations of both human and object bounding boxes.

Introducing paper: Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions
VOC 2012
Full name: The PASCAL Visual Object Classes Challenge 2012

see detailed use case on code implementation of the paper 'Tell Me Where To Look: Guided Attention Inference Networks'

Introducing paper: Tell Me Where to Look: Guided Attention Inference Network