Omkar2118/datasets huggingface dataset:
Locount

Loucount is a retail object detection and and counting dataset with rich annotations in retail stores, which consists of 50, 394 images with more than 1.9 million object instances in 140 categories

Introducing paper: Rethinking Object Detection in Retail Stores
VisRecall

Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far.

We propose a question-answering paradigm to study visualisation recallability and present VisRecall -- a novel dataset consisting of 200 information visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions from five question types, which are related to titles, filtering information, finding extrema, retrieving values, and understanding visualisations. It aims to make fundamental contributions towards a new generation of methods to assist designers in optimising information visualisations.

This dataset contains stimuli and collected participant data of VisRecall. The structure of the dataset is described in the README-File. If you are interested in related codes of the publication, please refer to the code repository in Metadata for Research Software.

Introducing paper: VisRecall: Quantifying Information Visualisation Recallability via Question Answering
PoserNet ECCV 2022 data

This data is derived from the 7Scenes dataset.
It contains graphs used for training PoserNet and for evaluating its performance.

Introducing paper: PoserNet: Refining Relative Camera Poses Exploiting Object Detections
RSBlur

The RSBlur dataset provides pairs of real and synthetic blurred images with ground truth sharp images. The dataset enables the evaluation of deblurring methods and blur synthesis methods on real-world blurred images.  Training, validation, and test sets consist of 8,878, 1,120, and 3,360 blurred images, respectively.

Introducing paper: Realistic Blur Synthesis for Learning Image Deblurring
UHDM

The first ultra-high-definition image demoireing dataset,  consisting of 4,500 4K resolution training pairs and 500 standard 4K resolution validation pairs.

Introducing paper: Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing
ELEVATER
Full name: Evaluation of Language-augmented Visual Task-level Transfer

The ELEVATER benchmark is a collection of resources for training, evaluating, and analyzing language-image models on image classification and object detection. ELEVATER consists of:

- Benchmark: A benchmark suite that consists of 20 image classification datasets and 35 object detection datasets, augmented with external knowledge
- Toolkit: An automatic hyper-parameter tuning toolkit; Strong language-augmented efficient model adaptation methods.
- Baseline: Pre-trained language-free and language-augmented visual models.
- Knowledge: A platform to study the benefit of external knowledge for vision problems.
- Evaluation Metrics: Sample-efficiency (zero-, few-, and full-shot) and Parameter-efficiency.
- Leaderboard: A public leaderboard to track performance on the benchmark

The ultimate goal of ELEVATER is to drive research in the development of language-image models to tackle core computer vision problems in the wild.

Introducing paper: ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Oracle-MNIST
Full name: Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms

We introduce the Oracle-MNIST dataset, comprising of 2828 grayscale images of 30,222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image noise and distortion. The training set totally consists of 27,222 images, and the test set contains 300 images per class. Oracle-MNIST shares the same data format with the original MNIST dataset, allowing for direct compatibility with all existing classifiers and systems, but it constitutes a more challenging classification task than MNIST. The images of ancient characters suffer from 1) extremely serious and unique noises caused by three-thousand years of burial and aging and 2) dramatically variant writing styles by ancient Chinese, which all make them realistic for machine learning research. The dataset is freely available at https://github.com/wm-bupt/oracle-mnist.

Introducing paper: Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms
HelixNet
Full name: HelixNet: A Dataset for Online LiDAR Segmentation

Large-scale and open-access LiDAR dataset intended for the evaluation of real-time semantic segmentation algorithms. In contrast to other large-scale datasets, HelixNet includes fine-grained data about the sensor's rotation and position, as well as the points' release time.

Introducing paper: Online Segmentation of LiDAR Sequences: Dataset and Algorithm
MNIST Multiview Datasets
Full name: MNIST Multiview Datasets

MNIST Multiview Datasets
========================

MNIST is a publicly available dataset consisting of 70, 000 images of handwritten digits distributed over
ten classes. We generated 2 four-view datasets where each view is a vector of R<sup>14 x 14</sup>:

* MNIST<sub>1</sub>:  It is generated by considering 4 quarters of image as 4 views. 
* MNIST<sub>2</sub>:  It is generated by  considering 4 overlapping views around the centre of images: this dataset brings redundancy between the views.


Related Papers:
```
Goyal, Anil, Emilie Morvant, Pascal Germain, and Massih-Reza Amini. 
"Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters."
Neurocomputing, 358, 2019, pp. 81-92.



Link to the ArXiv version:
https://arxiv.org/abs/1808.05784

Published Version:
https://doi.org/10.1016/j.neucom.2019.04.072
```

```
Goyal, Anil, Emilie Morvant, and Massih-Reza Amini. 
"Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization." 
In International Symposium on Intelligent Data Analysis, pp. 124-136. Springer, Cham, 2018.

Link to the ArXiv version: 
https://arxiv.org/abs/1805.10212

Published Version:
https://doi.org/10.1007/978-3-030-01768-2_11
```

Introducing paper: Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
KonIQ-10k
Full name: Konstanz Image Quality 10k Database

**KonIQ-10k** is a large-scale IQA dataset consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models.

Introducing paper: KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
KADID-10k

Konstanz artificially distorted image quality database (KADID-10k) contains 81 pristine images, each degraded by 25 distortions in 5 levels.

Introducing paper: DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning
HELMET

The HELMET dataset contains 910 videoclips of motorcycle traffic, recorded at 12 observation sites in Myanmar in 2016. Each videoclip has a duration of 10 seconds, recorded with a framerate of 10fps and a resolution of 1920x1080. The dataset contains 10,006 individual motorcycles, surpassing the number of motorcycles available in existing datasets. Each motorcycle in the 91,000 annotated frames of the dataset is annotated with a bounding box, and rider number per motorcycle as well as position specific helmet use data is available.

Introducing paper: Detecting motorcycle helmet use with deep learning
EgoProceL

EgoProceL is a large-scale dataset for procedure learning. It consists of 62 hours of egocentric videos recorded by 130 subjects performing 16 tasks for procedure learning. EgoProceL contains videos and key-step annotations for multiple tasks from CMU-MMAC, EGTEA Gaze+, and individual tasks like toy-bike assembly, tent assembly, PC assembly, and PC disassembly. EgoProceL overcomes the limitations of third-person videos. As, using third-person videos makes the manipulated object small in appearance and often occluded by the actor, leading to significant errors. In contrast, we observe that videos obtained from first-person (egocentric) wearable cameras provide an unobstructed and clear view of the action.

Introducing paper: My View is the Best View: Procedure Learning from Egocentric Videos
DEAP City Dataset

[comment]:<> (Data for the paper "Deciphering Environmental Air Pollution with Large Scale City Data")

## Main Dataset
`city_pollution_data.csv`

Relevant Columns:

* `Date`: Date of the sample
* `City`: City of the sample
* `X_median`: Median value of the pollutant/meteorological feature X for the day 
* `mil_miles`: Total vehicle travel distance for the sample
* `pp_feat`: Calculated feature for the influence of neighboring power plants
* `Population Staying at Home`: Used a measure of domestic emissions.

**Pollutants**:
`PM2.5`,`PM10`,`NO2`,`O3`,`CO`,`SO2`

**Meteorological Features**:
`Temperature`,`Pressure`,`Humidity`,`Dew`,`Wind Speed`,`Wind Gust`

    
## Power Plant Generation and Location Dataset [Extra]:
`pp_gen_data.csv`

Relevant Columns:

* `Month`: Month of the data
* `Netgen`: Net generation for that month.

If you find the data or code useful in your work, please cite
```
@inproceedings{ijcai2022p698,
  title     = {Deciphering Environmental Air Pollution with Large Scale City Data},
  author    = {Bhattacharyya, Mayukh and Nag, Sayan and Ghosh, Udita},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  year      = {2022},
}
```

Introducing paper: Deciphering Environmental Air Pollution with Large Scale City Data
AIH
Full name: Amodal InterHand

AIH is created for hand deocclusion and removal.

Introducing paper: 3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal
Genocide Transcript Corpus (GTC): Topic-Based Paragraph Classification in Genocide-Related Court Transcripts

The **Topic-Based Paragraph Classification in Genocide-Related Court Transcripts (GTC)** dataset is the first reference corpus annotated with samples from genocide tribunals in different international criminal courts. It is made up of witness statements about violence experienced. The material consists of 1475 text passages with about 40 to 120 pages per transcript, covering 3 tribunals: the Extraordinary Chambers in the Courts of Cambodia (ECCC) - 438 pages, the International Criminal Tribunal for Rwanda (ICTR) - 566 pages, and the International Criminal Tribunal of the Former Yugoslavia (ICTY) - 416 pages. As no datasets of any kind containing genocide court transcripts have been published nor other forms of pre-structured or annotated text data in this field of research exist, the aim was to address this gap by providing a systematically annotated dataset. 

Potential use cases include genocide-related inquiry conducted by those who need better to access, explore, and search through extensive documentation on these topics including researchers, lawyers and other practitioners. Broadly, its stated aim is to serve 3 purposes:

* (1) to provide a first reference corpus for the community
* (2) to establish benchmark performances (using state-of-the-art transformer-based approaches) for the new classification task of paragraph identification of violence-related
witness statements
* (3) to explore first steps towards transfer learning within the domain

Introducing paper: A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts
Visual Knowledge Tracing

**Visual Knowledge Tracing** contains images and human response data for a visual classification task on three datasets. Datasets are intended to serve as benchmark for visual knowledge tracing algorithms.

Introducing paper: Visual Knowledge Tracing