we have collected an annotated dataset that contains 600 fake images and 400 real images. All the fake images are generated by a generative adversarial net and all the real images are downsampled images from the ImageNet dataset. These images are evaluated by 10 workers from the Amazon Mechanical Turk (AMT) based on eight carefully defined attributes.

zu6yn4xgma0i/vTikz-human-annotated dataset hosted on Hugging Face and contributed by the HF Datasets community

This data is supplementary to the paper "AckSent: Human Annotated Dataset of Support and Sentiments in Dissertation Acknowledgments" .

HANNA, a large annotated dataset of Human-ANnotated NArratives for Automatic Story Generation (ASG) evaluation, has been designed for the benchmarking of automatic metrics for ASG. HANNA contains 1,056 stories generated from 96 prompts from the WritingPrompts dataset. Each prompt is linked to a human story and to 10 stories generated by different ASG systems. Each story was annotated on six human criteria (Relevance, Coherence, Empathy, Surprise, Engagement and Complexity) by three raters. HANNA also contains the scores produced by 72 automatic metrics on each story.

This is an open dataset of sentences from 19th and 20th century letterpress reprints of documents from the Hussite era. The dataset contains a corpus for language modeling and human annotations for named entity recognition (NER).

HumAID: Human-Annotated Disaster Incidents Data from Twitter

  Dataset Summary

The HumAID Twitter dataset consists of several thousands of manually annotated tweets that has been collected during 19 major natural disaster events including earthquakes, hurricanes, wildfires, and floods, which happened from 2016 to 2019 across different parts of the World. The annotations in the provided datasets consists of following humanitarian categories. The dataset consists only english… See the full description on the dataset page: https://huggingface.co/datasets/QCRI/HumAID-event-type.

These are supplementary materials for an open dataset of scanned images and OCR texts from 19th and 20th century letterpress reprints of documents from the Hussite era. The dataset contains human annotations for layout analysis, OCR evaluation, and language identification and is available at http://hdl.handle.net/11234/1-4615. These supplementary materials contain OCR texts from different OCR engines for book pages for which we have both high-resolution scanned images and annotations for OCR evaluation.

This is an open dataset of scanned images and OCR texts from 19th and 20th century letterpress reprints of documents from the Hussite era. The dataset contains human annotations for layout analysis, OCR evaluation, and language identification.

This dataset is a manually curated collection of structured data extracted from peer-reviewed food extrusion research articles. The dataset captures key parameters relevant to food extrusion processes, including equipment configurations, processing conditions, formulation details, and characterization methods. It is intended to support literature synthesis, meta-analyses, and knowledge representation in food extrusion research. This dataset provides a searchable, structured repository for researchers to efficiently access and analyse trends in food extrusion studies beyond what is typically available in standard academic databases. Lineage: This dataset was manually curated from 335 peer-reviewed food extrusion research articles sourced from the Web of Science database. The literature search used the following search syntax: "extru*" (Topic) AND "food" (Topic) NOT “packaging” (Topic). WoS Category filter: Food Science Technology, Nutrition & Dietetics, and Agriculture Dairy Animal Science. Key parameters—including equipment configurations, processing conditions, formulation details, and characterisation methods—were extracted, structured, and categorised by a domain expert in food engineering following a predefined schema. Citation screening was performed to ensure dataset quality.

This dataset contains human-annotated sense identifiers for 2562 contexts of 20 words used in the RUSSE'2018 shared task on Word Sense Induction and Disambiguation for the Russian language; part of the bts-rnc evaluation dataset. These sense identifiers are disambiguated as according to the sense inventory of the Large Explanatory Dictionary of Russian.

The annotation is done on December 1, 2017, on the Yandex.Toloka crowdsourcing platform. In particular, 80 pre-annotated contexts are used for training the human annotators, 2562 contexts are annotated by humans such that each context was annotated by 9 different annotators. The annotation reliability is indicated by a high value of Krippendorff's α = 0.83. After the annotation, every context was additionally inspected (“curated”) by the organizers of the shared task.

The following words are represented: акция (action / stock), байка (yarn / tale), гвоздика (carnation / nail), гипербола (hyperbole), град (avalanche), гусеница (grub), домино (domino), кабачок (marrow / pub), капот (hood), карьер (mine / career), кок (cook), крона (top / crown), круп (croup), мандарин (mandarine), рок (fate / rock), слог (syllable), стопка (glass, stack), таз (bowl), такса (rate / badger-dog), шах (shah / check).

The following files are included in this dataset:

Toloka assignments (training: tasks-train.tsv, annotation: tasks-test.tsv)

Toloka output (non-aggregated: assignments_01-12-2017.tsv.xz, aggregated: aggregated_results_pool_1036853_2017_12_01.tsv)

annotator agreement report (agreement.txt)

curated report (report-curated.tsv.xz and a supplementary file tasks-eval.tsv.xz)

the final aggregated dataset (bts-rnc-crowd.tsv)

The bts-rnc-crowd.tsv file has the following format: id, lemma, sense_id, left hand side context, word form, right hand side context, list of senses. The encoding is UTF-8 and the line breaks are LF (UNIX).

This dataset consists of 17,300 images and includes two annotation files, each in a distinct format. The first file, labeled "Label," contains annotations at their original scale, while the second file, named "yolo_format_labels," provides annotations in YOLO format. The dataset was created by utilizing the OIDv4 toolkit, a specialized tool for gathering data from Google Open Images. It's important to emphasize that this dataset is exclusively dedicated to human detection.

This dataset is a valuable resource for training deep learning models tailored for human detection tasks. The images in the dataset are of high quality and have been meticulously annotated with bounding boxes encompassing the regions where humans are present. Annotations are available in two formats: the original scale, which represents pixel coordinates of the bounding boxes, and the YOLO format, which represents bounding box coordinates in a normalized form.

The dataset was carefully curated by scraping relevant images from Google Open Images using the OIDv4 toolkit. Only images relevant to human detection tasks were included. Consequently, it is an ideal choice for training deep learning models specifically designed for human detection tasks.

PlainFact is a high-quality human-annotated dataset with fine-grained explanation (i.e., added information) annotations designed for Plain Language Summarization tasks, along with PlainQAFact factuality evaluation framework. It is collected from the Cochrane database sampled from CELLS dataset (Guo et al., 2024). PlainFact is a sentence-level benchmark that splits the summaries into sentences with fine-grained explanation annotations. In total, we have 200 plain language summary-abstract… See the full description on the dataset page: https://huggingface.co/datasets/uzw/PlainFact.

This folder contains pictures of radishes collected on the PMF experimental field during Spring 2017. There are two kinds of labeled images in the following folders:

- human annotations: human annotators draw polygons around each plant and those were then refined using an active contours algorithm.
- machine annotations: An SVM trained on the human annotations was used to produce labeled images. Images with bad segmentation were manually discarded.

Each of these folder contains an images folder containing original pictures and a labels folder containing binary segmentation masks.

Human Object Dectection

## Overview

Human Object Dectection is a dataset for object detection tasks - it contains Humans annotations for 1,980 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

1. Overview This dataset is a collection of 50,000+ images of Human full body with multiple attributes that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Annotated Imagery Data of human in full body images This dataset contains 50,000+ images of human in full body. The dataset has been annotated in face bounding box face, body bounding box and Attribute of mask, wheelchair, stroller, umbrella, suitcase, bag, backpack, laptop, cellphone,...

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands.

Dataset corresponding to the ICASSP 2024 paper "Crowdsourced and Automatic Speech Prominence Estimation" [link]

This dataset is useful for training machine learning models to perform automatic emphasis annotaiton, as well as downstream tasks such as emphasis-controlled TTS, emotion recognition, and text summarization. The dataset is described in Section 3 (Emphasis Annotation Dataset). The contents of this section are copied below for convenience.

We used our crowdsourced annotation system to perform human annotation on one eighth of the train-clean-100 partition of the LibriTTS [1] dataset. Specifically, participants annotated 3,626 utterances with a total length of 6.42 hours and 69,809 words from 18 speakers (9 male and 9 female). We collected at least one annotation of all 3,626 utterances, at least two annotations of 2,259 of those utterances, at least four annotations of 974 utterances, and at least eight annotations of 453 utterances. We did this in order to explore (in Section 6) whether it is more cost-effective to train a system on multiple annotations of fewer utterances or fewer annotations of more utterances. We paid 298 annotators to annotate batches of 20 utterances, where each batch takes approximately 15 minutes. We paid $3.34 for each completed batch (estimated $13.35 per hour). Annotators each annotated between one and six batches. We recruited on MTurk US residents with an approval rating of at least 99 and at least 1000 approved tasks. Today, microlabor platforms like MTurk are plagued by automated task-completion software agents (bots) that randomly fill out surveys. We filtered out bots by excluding annotations from an additional 107 annotators that marked more than 2/3 of words as emphasized in eight or more utterances of the 20 utterances in a batch. Annotators who fail the bot filter are blocked from performing further annotation. We also recorded participants' native country and language, but note these may be unreliable as many MTurk workers use VPNs to subvert IP region filters on MTurk [2].

The average Cohen Kappa score for annotators with at least one overlapping utterance is 0.226 (i.e., ``Fair'' agreement)---but not all annotators annotate the same utterances, and this overemphasizes pairs of annotators with low overlap. Therefore, we use a one-parameter logistic model (i.e., a Rasch model) computed via py-irt [3], which predicts heldout annotations from scores of overlapping annotators with 77.7% accuracy (50% is random).

The structure of this dataset is a single JSON file of word-aligned emphasis annotations. The JSON references file stems of the LibriTTS dataset, which can be found here. All code used in the creation of the dataset can be found here. The format of the JSON file is as follows.

{

{ "annotations": [ { "score": [ , , ... ], "stem": , "words": [ [ , ,

], [ , ,

], ... ] }, ... ], "country": , "language": }, ... }

[1] Zen et al., “LibriTTS: A corpus derived from LibriSpeech for text-to-speech,” in Interspeech, 2019.[2] Moss et al., “Bots or inattentive humans? Identifying sources of low-quality data in online platforms,” PsyArXiv preprint PsyArXiv:wr8ds, 2021.[3] John Patrick Lalor and Pedro Rodriguez, “py-irt: A scalable item response theory library for Python,” INFORMS Journal on Computing, 2023.

50,000+ human full body Annotated Imagery images with multiple attributes ready for AI & ML models

This dataset is for detecting the ears of humans. In this dataset, an annotation file of ears is also available for training and validation images in JSON format.

This dataset will helpful for detecting face and ears as well, using maskRCNN and YOLO.

• train and val folders are of images.
• train_json.json is annotation file of train images.
• val_json.json is annotation file of val images

This dataset contains images of human faces with annotated ear regions. The dataset consists of a total of 440 images, with each image containing one or more human faces. The dataset is useful for developing computer vision models for ear detection in human faces, which can be used in various applications such as biometric authentication and surveillance. The images were collected from various sources and have varying resolutions and lighting conditions. The dataset includes a CSV file with annotations for the ear regions in each image.

Background The recent draft assembly of the human genome provides a unified basis for describing genomic structure and function. The draft is sufficiently accurate to provide useful annotation, enabling direct observations of previously inferred biological phenomena. Results We report here a functionally annotated human gene index placed directly on the genome. The index is based on the integration of public transcript, protein, and mapping information, supplemented with computational prediction. We describe numerous global features of the genome and examine the relationship of various genetic maps with the assembly. In addition, initial sequence analysis reveals highly ordered chromosomal landscapes associated with paralogous gene clusters and distinct functional compartments. Finally, these annotation data were synthesized to produce observations of gene density and number that accord well with historical estimates. Such a global approach had previously been described only for chromosomes 21 and 22, which together account for 2.2% of the genome. Conclusions We estimate that the genome contains 65,000-75,000 transcriptional units, with exon sequences comprising 4%. The creation of a comprehensive gene index requires the synthesis of all available computational and experimental evidence.

Dataset Card for MPII Human Pose

MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation. The dataset includes around 25K images containing over 40K people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/MPII_Human_Pose_Dataset.