Omkar2118/datasets huggingface dataset:
LTFT
Full name: Long-Term Face Tracking

Dataset originally conceived for multi-face tracking/detection for highly crowded scenarios. In these scenarios, the face is the only part that can be used to track the individuals.

All our videos present novel crowd scenes recorded at near-eye level, where faces are visible enough to be analysed at the microscopic level, while also benefiting from a macroscopic view of the crowd. It includes:

- Face detections of 715 unique subjects along with instructions to download the synchronized video.

- More than 75k face detections annotated.

- A density ranging from 3 to 13 people/frame.

- 6 indoor and 4 outdoor videos. 8/10 videos are totally unconstrained, 2/10 feature 3 re-appearances per subject.

Our dataset may be useful for:

- Face tracking, especially relevant for crowded scenarios (typically from video-surveillance cameras).

- Heavily occluded body tracking (in many videos, only the face is mostly visible).

- Face recognition.

- Face detection for partially occluded faces.

Introducing paper: Rank-based verification for long-term face tracking in crowded scenes
IMS Bearing Dataset

Bearing acceleration data from three run-to-failure experiments on a loaded shaft. The data set was provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati.
PRONOSTIA Bearing Dataset

The PRONOSTIA (also called FEMTO) bearing dataset consists of 17 accelerated run-to-failures on a small bearing test rig. Both acceleration and temperature data was collected for each experiment.

The dataset was used in the 2012 IEEE Prognostic Challenge. The dataset is from FEMTO-ST Institute in France.
Makeup216

Makeup216 contains a variety and representation of logo (captured from the real world) and is among the largest and most complex logo datasets in the field. It comprises of 216 logos and 157 brands, including 10,019 images and 37,018 annotated logo objects.

Introducing paper: Makeup216: Logo Recognition with Adversarial Attention Representations
Drosophila Immunity Time-Course Data

The data used for all results in this paper can be found [here](https://github.com/sara-venkatraman/Bayesian-Gene-Dynamics/tree/master/Data). This directory contains:

* `GeneData.csv`: Contains temporal gene expression measurements for 1735 genes at 17 time points. Measurements are provided as the $\log_2$-fold change from first time point. Hours corresponding to each time point are defined in the R script [`3_Results.R`](https://github.com/sara-venkatraman/Bayesian-Gene-Dynamics/blob/master/3_Results.R#L23) in our GitHub repository. This dataset is derived from a larger gene expression dataset collected by [Schlamp et al. (2021)](https://www.ncbi.nlm.nih.gov/bioproject/PRJNA641552). 
* `PriorMatrix.csv`: A 1735 x 1735 prior adjacency matrix. Each entry is `0`, `1`, or `NA` to indicate that a biological relationship between the corresponding two genes is unlikely, likely, or unknown according to external databases.

Further details about the collection of this data can be found in Section 4.1 and Appendix C of our paper. The R script `3_Results.R` shows how these CSV files are read and used for our analysis.
VGG-Sound Sync

**VGG-Sound Sync** is an audio-visual synchronisation benchmark based on videos collected from YouTube. VGG-Sound Sync contains over 100k video clips, spanning 160 classes and can be downloaded [here](https://www.robots.ox.ac.uk/~vgg/research/avs/data/vggsoundsync.csv).

Note, only the test clips are included here, please use the training clips in the original [VGG-Sound](https://paperswithcode.com/dataset/vgg-sound) to train your models ( classes are same with the ones in the test clips). Each line in the json file has been defined by:

`# YouTube ID, start seconds, label `

Introducing paper: Audio-Visual Synchronisation in the wild
BRATS 2021
Full name: RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021

The RSNA-ASNR-MICCAI BraTS 2021 challenge utilizes multi-institutional pre-operative baseline multi-parametric magnetic resonance imaging (mpMRI) scans, and focuses on the evaluation of state-of-the-art methods for (Task 1) the segmentation of intrinsically heterogeneous brain glioblastoma sub-regions in mpMRI scans. Furthemore, this BraTS 2021 challenge also focuses on the evaluation of (Task 2) classification methods to predict the MGMT promoter methylation status.