Omkar2118/datasets huggingface dataset:
SuperMUDI

The Super-resolution of Multi-Dimensional Diffusion MRI
(Super MUDI) dataset  contains the data of four healthyhuman subjects with ages range between 19 and 46 years.
For each subject 1,344 MRI volumes are provided. Theimaging device was clinical 3T Philips Achieva Scanner
(Best, Netherlands) with a 32-channel adult head coil.
The Super MUDI Challenge comprises two tasks:
isotropic, and anisotropic super-resolution. The names of
these tasks were derived from the acquisition strategies of
the low-resolution MRI data. The objective of using two
down-sampling strategies is to compare the combinations of
the down-sampling methods and the super-resolution
approaches that can best to be used in a clinical scheme to
obtain simulated high-quality and high-fidelity MRI images
while reducing the acquisition time. In the anisotropic
subsampling the volume has high in-plane resolution
(2.5mm ×2.5mm), but thick axial slice (5mm), while in the
isotropic subsampling the volume has low resolution (5mm)
in all the directions.
For our experiments, we use one subject each for training
and validation, and two for testing.
Reference: Marco Pizzolato, Marco Palombo, Jana Hutter, Vish-
wesh Nash, Fan Zhang, and Noemi Gyori, “Super-
resolution of Multi Dimensional Diffusion MRI data,”
Mar. 2020
Violent-Flows

Crowd Violence \ Non-violence Database and benchmark: A database of real-world, video footage of crowd violence, along with standard benchmark protocols designed to test both violent/non-violent classification and violence outbreak detections. The data set contains 246 videos. All the videos were downloaded from YouTube. The shortest clip duration is 1.04 seconds, the longest clip is 6.52 seconds, and the average length of a video clip is 3.60 seconds.

Introduced in:
Tal Hassner, Yossi. Itcher, and Orit Kliper-Gross, Violent Flows: Real-Time Detection of Violent Crowd Behavior, 3rd IEEE International Workshop on Socially Intelligent Surveillance and Monitoring (SISM) at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Rhode Island, June 2012 .