Omkar2118/datasets huggingface dataset:
ScanNet200

The ScanNet200 benchmark studies 200-class 3D semantic segmentation - an order of magnitude more class categories than previous 3D scene understanding benchmarks. The source of scene data is identical to ScanNet, but parses a larger vocabulary for semantic and instance segmentation

Introducing paper: Language-Grounded Indoor 3D Semantic Segmentation in the Wild
Real-time Election Results: Portugal 2019 Data Set

Data Set Information:

A data set describing the evolution of results in the Portuguese Parliamentary Elections of October 6th 2019.
The data spans a time interval of 4 hours and 25 minutes, in intervals of 5 minutes, concerning the results of the 27 parties involved in the electoral event.
The data set is tailored for predictive modelling tasks, mostly focused on numerical forecasting tasks.
Regardless, it allows for other tasks such as ordinal regression or learn-to-rankProvide a short description of your data set (less than 200 characters).

Additional (and updated) information may be found in [Web Link] :
- Raw data sets
- R code to build the final data set
- Basic operations to build predictive modelling tasks using this data set


Attribute Information:

TimeElapsed (Numeric): Time (minutes) passed since the first data acquisition
time (timestamp): Date and time of the data acquisition
territoryName (string): Short name of the location (district or nation-wide)
totalMandates (numeric): MP's elected at the moment
availableMandates (numeric): MP's left to elect at the moment
numParishes (numeric): Total number of parishes in this location
numParishesApproved (numeric): Number of parishes approved in this location
blankVotes (numeric): Number of blank votes
blankVotesPercentage (numeric): Percentage of blank votes
nullVotes (numeric): Number of null votes
nullVotesPercentage (numeric): Percentage of null votes
votersPercentage (numeric): Percentage of voters
subscribedVoters (numeric): Number of subscribed voters in the location
totalVoters (numeric): Percentage of blank votes
pre.blankVotes (numeric): Number of blank votes (previous election)
pre.blankVotesPercentage (numeric): Percentage of blank votes (previous election)
pre.nullVotes (numeric): Number of null votes (previous election)
pre.nullVotesPercentage (numeric): Percentage of null votes (previous election)
pre.votersPercentage (numeric): Percentage of voters (previous election)
pre.subscribedVoters (numeric): Number of subscribed voters in the location (previous election)
pre.totalVoters (numeric): Percentage of blank votes (previous election)
Party (string): Political Party
Mandates (numeric): MP's elected at the moment for the party in a given district
Percentage (numeric): Percentage of votes in a party
validVotesPercentage (numeric): Percentage of valid votes in a party
Votes (numeric): Percentage of party votes
Hondt (numeric): Number of MP's according to the distribution of votes now
FinalMandates (numeric): Target: final number of elected MP's in a district/national-level


Relevant Papers:

Nuno Moniz (2019) Real-time 2019 Portuguese Parliament Election Results Dataset. arXiv

Code + Data in [Web Link]



Citation Request:

Nuno Moniz (2019) Real-time 2019 Portuguese Parliament Election Results Dataset. arXiv
YouTube-Hands

**YouTube-Hands**  includes 240 videos which are annotated with hand trajectories.

Introducing paper: Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild
MSU NR VQA Database
Full name: MSU No-Reference Video Quality Assessment Database

The dataset was created for video quality assessment problem. It was formed with 36 clips from Vimeo, which were selected from 18,000+ open-source clips with high bitrate (license CCBY or CC0). 

The clips include videos recorded by both professionals and amateurs. Almost half of the videos contain scene changes and high dynamism. Moreover, the synthetic to natural lightning ratio is approximately 1 to 3.

* Content type: nature, sport, humans close up, gameplays, music videos, water stream or steam, CGI
* Effects and distortions: shaking, slow-motion, grain/noisy, too dark/bright regions, macro shooting, captions (text), extraneous objects on the camera lens or just close to it
* Resolution: 1920x1080 as the most popular modern video resolution (more in the future)
* Format: yuv420p
* FPS: 24, 25, 30, 39, 50, 60
* Videos duration: mainly 10 seconds

Such content diversity helps simulate near-realistic conditions.
The choice of videos collected for the benchmark dataset employed clustering in terms of space-time complexity to obtain a representative distribution.

For compression we used 40 codecs of 10 compression standards (H.264, AV1, H.265, VVC, etc.). Each video was compressed with 3 target bitrates: 1,000 Kbps, 2,000 Kbps, and 4,000 Kbps, and different real-life encoding modes: constant quality (CRF) and variable bitrate (VBR). The choice of bitrate range simplifies the subjective comparison procedure since the video quality is more difficult to distinguish visually at higher bitrates. 

The subjective assessment involved pairwise comparisons using crowdsourcing service Subjectify.us. To increase the relevance of the results, each pair of videos received at least 10 responses from participants. In total, 766362 valid answers were collected from more than 10800 unique participants.

Introducing paper: Video compression dataset and benchmark of learning-based video-quality metrics
PCSOD

It is a new proposed dataset for point cloud salient object detection that has 2000 training samples and 872 testing samples.

Introducing paper: Salient Object Detection for Point Clouds
VITON-HD
Full name: High-Resolution VITON-Zalando Dataset

**VITON-HD** dataset is a dataset for high-resolution (i.e.,  1024x768) virtual try-on of clothing items. Specifically, it consists of 13,679 frontal-view woman and top clothing image pairs.

Introducing paper: VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization
AnimeCeleb
Full name: AnimeCeleb

We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize 3D animation models as the controllable image samplers, which can provide a large amount of head images with their corresponding detailed pose annotations. To facilitate a data creation process, we build a semi-automatic pipeline leveraging an open 3D computer graphics software with a developed annotation system. After training with the AnimeCeleb, recent head reenactment models produce high-quality animation head reenactment results, which are not achievable with existing datasets. Furthermore, motivated by metaverse application, we propose a novel pose mapping method and architecture to tackle a cross-domain head reenactment task. During inference, a user can easily transfer one's motion to an arbitrary animation head. Experiments demonstrate the usefulness of the AnimeCeleb to train animation head reenactment models, and the superiority of our cross-domain head reenactment model compared to state-of-the-art methods. Our dataset and code are available at https://github.com/kangyeolk/AnimeCeleb.

Introducing paper: AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment
GBUSV
Full name: Gallbladder Ultrasound Videos

### Description

GBUSV is a un-annotated dataset consisting of ultrasound videos of of patients with either of a malignant or a non-malignant gallbladder. The ultrasound videos were obtained from patients referred to the radiology department of PGIMER, Chandigarh (a high-input hospital in Northern India) for abdominal ultrasound examinations of suspected gallbladder pathologies. Patients were at fasting of at least 6 hours. A 1-5 MHz curved array transducer (C-1-5D, Logiq S8, GE Healthcare) was used. The scanning intended to include the entire gallbladder and the lesion or pathology. The length of the video sequences varies from 43 to 888 frames. The dataset consists of 32 malignant and 32 non-malignant videos containing a total of 12,251 and 3,549 frames, respectively. The video frames are cropped from the center to anonymize the patient information and annotations. The processed frame sizes are of size 360x480 pixels.


### Annotations

The images of the video sequences are un-annotated and suitable for unsupervised learning tasks. We provide the high-level categorization for each video whether it's malignant or non-malignant.

Introducing paper: Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining
DNA mutations

In bioinformatics, the issue of mutation discovery and type determination remains a significant concern. The problem is divided by the researchers into binary classification and multi-class problems. When the user wants to know if the DNA sequence has been altered, the issue is a binary classification problem. When it is desirable to identify the principal class of mutation or its sub-classes, the problem becomes more challenging. The primary classes of mutations are deletion, insertion, and replacement mutation, and their sub-classes are (deletion frameshift, deletion in-frame, insertion frameshift, insertion in-frame, silent, missense, nonsense, and read-through). Additionally, answers to sporadic issues like the DNA sequence alignment challenge are necessary for mutation detection techniques.
Due to the scarcity of labeled databases, this data set was created by addressing an unlabeled database and creating random mutations of all kinds for the purpose of benefiting from them by researchers in the field of bioinformatics to analyze DNA sequences and know the impact of mutations on humans. By using a database published on the NCBI GeneBank website (NC_045512.2 Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome).
NMED-H
Full name: Naturalistic Music EEG Dataset - Hindi

The NMED-H dataset contains scalp EEG responses recorded from 48 adults as they heard intact and scrambled
versions of full-length vocal works (Hindi pop songs). Sixteen stimuli were included in the experiment: Four songs
in four conditions per song. Twelve participants were assigned to each stimulus, and each participant heard their
assigned stimuli twice (24 trials total per stimulus). Dense-array EEG was recorded using the Electrical
Geodesics, Inc. (EGI) GES 300 platform. Data are published in Matlab format. The dataset contains (1) raw EEG
(individual recordings, 97 files), (2) clean EEG (aggregated by stimulus and listen, 32 files), (3) spatially filtered
EEG (aggregated by stimulus condition, four files), (4) behavioral responses (grouped by listen, two files), and (5)
participant-stimulus assignment file. Items (1) - (3) are compressed in .zip archives (500 MB - 2 GB each); an
example file from each archive can be downloaded separately. Items (4) and (5) are < 1 KB each. This dataset
can be used in combination with the Naturalistic Music EEG Dataset - Tempo (NMED-T).