Omkar2118/datasets huggingface dataset:
HPD
Full name: Head-Pose Detection

These images were generated using Blender and IEE-Simulator with different head-poses, where the images are labelled according to nine classes (straight, turned bottom-left, turned left, turned top-left, turned bottom-right, turned right, turned top-right, reclined, looking up). The dataset contains 16,013 training images and 2,825 testing images, in addition to 4,700 images for improvements.

Introducing paper: Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
H-DIBCO 2016

H-DIBCO 2016 is the international Handwritten Document Image Binarization Contest organized in the context of ICFHR 2016 conference
DIBCO 2011

DIBCO 2011 is the International Document Image Binarization Contest organized in the context of ICDAR 2011 conference. The general objective of the contest is to identify current advances in document image binarization for both machine-printed and handwritten document images using evaluation performance measures that conform to document image analysis and recognition.
DIBCO 2017

DIBCO 2017 is the international Competition on Document Image Binarization organized in conjunction with the ICDAR 2017 conference. The general objective of the contest is to identify current advances in document image binarization of machine-printed and handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements
CB-ToF-Extension
Full name: Extension of Cornell-Box Time-of-Flight Dataset

An extension of the Cornell-Box Time-of-Flight Dataset (https://paperswithcode.com/dataset/cb-tof) containing moving objects. It follows the same data structure.

Introducing paper: Weakly-Supervised Optical Flow Estimation for Time-of-Flight
DIBCO 2013

DIBCO 2013 is the international Document Image Binarization Contest organized in the context of ICDAR 2013 conference. The general objective of the contest is to identify current advances in document image binarization for both machine-printed and handwritten document images using evaluation performance measures that conform to document image analysis and recognition.
H-DIBCO 2014

H-DIBCO 2014 is the International Document Image Binarization Competition which is dedicated to handwritten document images organized in conjunction with ICFHR 2014 conference. The objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures.
LRDE DBD

This is a dataset is composed of full-document images, groundtruth, and tools to perform an evaluation of binarization algorithms. It allows pixel-based accuracy and OCR-based evaluations.
H-DIBCO 2018

H-DIBCO 2018 is the international Handwritten Document Image Binarization Contest organized in the context of ICFHR 2018 conference. The general objective of the contest is to record recent advances in document image binarization using established evaluation performance measures.
H-DIBCO 2012

H-DIBCO 2012 is the International Document Image Binarization Competition which is dedicated to handwritten document images organized in conjunction with ICFHR 2012 conference. The objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures.
Acoustic frequency responses in a conventional classroom

Hahmann, Manuel; Verburg Riezu, Samuel Arturo (2021): Acoustic frequency responses in a conventional classroom. Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.13315286
SST-2

The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.

Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.

Introducing paper: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Occluded COCO

**Occluded COCO** is automatically generated subset of COCO val dataset, collecting partially occluded objects for a large variety of categories in real images in a scalable manner, where target object is partially occluded but the segmentation mask is connected.

Introducing paper: A Tri-Layer Plugin to Improve Occluded Detection
Separated COCO

**Separated COCO** is automatically generated subsets of COCO val dataset, collecting separated objects for a large variety of categories in real images in a scalable manner, where target object segmentation mask is separated into distinct regions by the occluder.

Introducing paper: A Tri-Layer Plugin to Improve Occluded Detection
TREC-News

The TREC News Track features modern search tasks in the news domain. In partnership with The Washington Post, we are developing test collections that support the search needs of news readers and news writers in the current news environment. It's our hope that the track will foster research that establishes a new sense for what "relevance" means for news search.
Robust04

The goal of the Robust track is to improve the consistency of retrieval technology by focusing on poorly performing topics. In addition, the track brings back a classic, ad hoc retrieval task in TREC that provides a natural home for new participants. An ad hoc task in TREC investigates the performance of systems that search a static set of documents using previously-unseen topics. For each topic, participants create a query and submit a ranking of the top 1000 documents for that topic.
Webis-Touché-2020

This paper is a condensed report on the second year of the Touché shared task on argument retrieval held at CLEF 2021. With the goal to provide a collaborative platform for researchers, we organized two tasks: (1) supporting individuals in finding arguments on controversial topics of social importance and (2) supporting individuals with arguments in personal everyday comparison situations.
DIS5K
Full name: Dichotomous Image Segmentation (DIS) Dataset

To build the highly accurate Dichotomous Image Segmentation dataset (DIS5K), we first manually collected over 12,000 images from Flickr1 based on our pre-designed keywords. Then, we obtained 5,470 images of 22 groups and 225 categories from the 12,000 images according to the structural complexities of the objects. Each image is then manually labeled with pixel-wise accuracy using GIMP. The labeled targets in DIS5K mainly focus on the “objects of the images defined by the pre-designed keywords (categories)” regardless of their characteristics e.g., salient, common, camouflaged, meticulous, etc. The average per-image labeling time is ∼30 minutes and some images cost up to 10 hours.

Introducing paper: Highly Accurate Dichotomous Image Segmentation
detection_of_IoT_botnet_attacks_N_BaIoT Data Set
Full name: detection_of_IoT_botnet_attacks_N_BaIoT Data Set

This dataset addresses the lack of public botnet datasets, especially for the IoT. It suggests *real* traffic data, gathered from 9 commercial IoT devices authentically infected by Mirai and BASHLITE.