This is a dataset of ground truth annotations for benchmark data provided in A. Sielaff, D. Mangini, O. Kabov, M. Raza, A. Garivalis, M. Zupančič, S. Dehaeck, S. Evgenidis, C. Jacobs, D. Van Hoof, O. Oikonomidou, X. Zabulis, P. Karamaounas, A. Bender, F. Ronshin, M. Schinnerl,

J. Sebilleau, C. Colin, P. Di Marco, T. Karapantsios, I. Golobič, A. Rednikov, P. Colinet, P. Stephan, L. Tadrist, The multiscale boiling investigation on-board the international space station:

An overview, Applied Thermal Engineering 205 (2022) 117932. doi:10.1016/j.applthermaleng.2021.117932.

The annotations regard the 15 image sequences provided in the benchmark data and denoted as D1-D15.

The annotators were asked to localize the contact points and points on the bubble boundary so an adequate contour identification is provided, according to the judgement of the expert. The annotators were two multiphase dynamics experts (RO, SE) and one image processing expert (ICS). The annotators used custom-made software to pinpoint samples upon contour locations in the images carefully, using magnification, undo, and editing facilities. The experts annotated the contact points and multiple points on the contour of the bubble until they were satisfied with the result.

The annotations were collected for the first bubble of each sequence. For each bubble, 20 frames were sampled in chronological order and in equidistant temporal steps and annotated. All experts annotated data sets D1-D15. The rest were annotated by ICS after learning annotation insights from the multiphase dynamics experts.

The format of the dataset is as follows. A directory is dedicated to each bubble annotation. The directory name notes the number of the dataset and the annotator id. Each directory contains 20 text files and 20, corresponding, images. Each text file contains a list with the 2D coordinates of one bubble annotation. The first coordinate marks the left contact point and the last coordinate marks the right contact point. These coordinates refer to a corresponding image contained in the same directory. Text files and image files are corresponded through their file names, which contain the frame number. The frame number refers to the image sequence. Images are in lossless PNG format.

This is the data set related to the paper "Language Model Assisted OCR Classification for Republican Chinese Newspaper Text", JDADH 11/2023. In this work, we present methods to obtain a neural optical character recognition (OCR) tool for article blocks in a Republican Chinese newspaper. The dataset contains two subsets: The pairs of text block crops and corresponding ground truth annotations from April 1920, 1930 and 1939 of the Jingbao newspaper (jingbao_annotated_crops.zip). The labeled images of single characters which we automatically cropped from the April 1939 issues of the Jingbao using separators generated from projection profiles (jingbao_char_imgs.zip).

This repository contains ground truth data for 18 datasets that are collected from 7 Biodiversity data portals. We manually annotated the metadata fields according to the Biodiversity Metadata Ontology (BMO) ontology. This ground truth is used to evaluate the developed Meta2KG approach that is used to transform raw metadata filed into RDF.

This poster is related to the following paper: U. Sampathkumar, V. B. S. Prasath, S. Meena, K. Palaniappan. Assisted Ground truth generation using Interactive Segmentation on a Visualization and Annotation Tool. IEEE Applied Imagery Pattern Recognition (AIPR), Washington DC, USA.The video contains a demo of the interactive image segmentation within the Firefly tool.

This dataset contains the ground truth data used to evaluate the musical pitch, tempo and key estimation algorithms developed during the AudioCommons H2020 EU project and which are part of the Audio Commons Audio Extractor tool. It also includes ground truth information for the single-eventness audio descriptor also developed for the same tool. This ground truth data has been used to generate the following documents: Deliverable D4.4: Evaluation report on the first prototype tool for the automatic semantic description of music samples Deliverable D4.10: Evaluation report on the second prototype tool for the automatic semantic description of music samples Deliverable D4.12: Release of tool for the automatic semantic description of music samples All these documents are available in the materials section of the AudioCommons website. All ground truth data in this repository is provided in the form of CSV files. Each CSV file corresponds to one of the individual datasets used in one or more evaluation tasks of the aforementioned deliverables. This repository does not include the audio files of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed. Structure of the CSV files All CSV files in this repository (with the sole exception of SINGLE EVENT - Ground Truth.csv) feature the following 5 columns: Audio reference: reference to the corresponding audio file. This will either be a string withe the filename, or the Freesound ID (for one dataset based on Freesound content). See below for details about how to obtain those files. Audio reference type: will be one of Filename or Freesound ID, and specifies how the previous column should be interpreted. Key annotation: tonality information as a string with the form "RootNote minor/major". Audio files with no ground truth annotation for tonality are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10. Tempo annotation: tempo information as an integer representing beats per minute. Audio files with no ground truth annotation for tempo are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10. Note that integer values are used here because we only have tempo annotations for music loops which typically only feature integer tempo values. Pitch annotation: pitch information as an integer representing the MIDI note number corresponding to annotated pitch's frequency. Audio files with no ground truth pitch for tempo are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10. The remaining CSV file, SINGLE EVENT - Ground Truth.csv, has only the following 2 columns: Freesound ID: sound ID used in Freesound to identify the audio clip. Single Event: boolean indicating whether the corresponding sound is considered to be a single event or not. Single event annotations were collected by the authors of the deliverables as described in deliverable D4.10. How to get the audio data In this section we provide some notes about how to obtain the audio files corresponding to the ground truth annotations provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these ground truth annotations. Apple Loops (APPL): This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are provided here. Carlos Vaquero Instruments Dataset (CVAQ): This dataset includes single instrument recordings carried out by Carlos Vaquero as part of this master thesis. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs Freesound Loops 4k (FSL4): This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are provided here. Giant Steps Key Dataset (GSKY): This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations available here. Good-sounds Dataset (GSND): This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are available here. University of IOWA Musical Instrument Samples (IOWA): This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by visiting this website. Mixcraft Loops (MIXL): This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to these loops requires owning

CGHD

This dataset contains images of hand-drawn electrical circuit diagrams as well as accompanying annotation and segmentation ground-truth files. It is intended to train (e.g. ANN) models for extracting electrical graphs from raster graphics.

Content

• 3.269 Annotated Raw Images
  
  • 31 Main Drafters
    • 12 Circuits per Drafter
    • 2 Drawings per Circuit
    • 4 Photos per Drawing
  • Additional Circuit Images provided by TU Dresden (from Real-World Examinations, Drafter 0)
  • Additional Circuit Images provided by RPTU Kaiserslautern-Landau (Drafter -1)
  • 248.020 Bounding Box Annotations
  • 40.711 Rotation Annotations
  • 1.437 Mirror Annotations
  • 85.417 Text String Annotations (equals 93.74% completeness)
    
    • 289.850 Text Characters
    • 98 Character Types (Upper/Lower Case Latin, Numbers, Special Characters)
• 320 Binary Segmentation Maps
  
  • Strokes vs. Background
  • Accompanying Polygon Annotation Files
  • 22.929 Polygon Annotations
• 59 Object Classes
• Scripts for Data Loading, Statistics, Consistency Check and Training Preparation

Test Set Ground Truth

## Overview

Test Set Ground Truth is a dataset for object detection tasks - it contains Test Set Ground Truth annotations for 259 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).

This dataset contains the 16 bit of the manually annotated ground truth labels for the nuclei that were used both in training (Labelled as "Original") or inference (Labelled as "Biological" or "Technical) for the MRCNN and FPN2-WS networks

Ground Truth (7PM 8PM)

## Overview

Ground Truth (7PM 8PM) is a dataset for object detection tasks - it contains Helmets annotations for 275 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).

This dataset provides manually annotated ground truth for entity linking over public procurement data from the Spend Network corpus, using Wikidata as the target knowledge graph.

It includes:

• SN_input_table.csv: A structured table containing procurement records, where each row represents a contracting authority (buyer) with related address information (street, locality, postcode, country)
• SN_GT.csv: Ground truth annotations specifying which cells (identified by table, row, and column) are linked to Wikidata entities. The dataset also includes NIL entries for cases where no suitable entity could be found in Wikidata.

This resource supports the evaluation of entity linking systems in a realistic setting where NIL detection is essential. It has been developed in the context of the enRichMyData project and is designed for semantic enrichment and benchmarking tasks involving tabular data.

Dataset

This dataset was created by Harrison Chapman

Contents

It contains the following files:

CGHD

This dataset contains images of hand-drawn electrical circuit diagrams as well as accompanying annotation and segmentation ground-truth files. It is intended to train (e.g. ANN) models for extracting electrical graphs from raster graphics.

Content

3.173 Annotated Raw Images

30 Drafters

12 Circuits per Drafter

2 Drawings per Circuit

4 Photos per Drawing

Additional Circuit Images provided by TU Dresden (from Real-World Examinations, Drafter 0)

Additional Circuit Images provided by RPTU Kaiserslautern-Landau (Drafter -1)

245.962 Bounding Box Annotations

39.955 Rotation Annotations

1.339 Mirror Annotations

84.431 Text String Annotations (equals 93.41% completeness)

286.467 Text Characters

98 Character Types (Upper/Lower Case Latin, Numbers, Special Characters)

284 Binary Segmentation Maps

Strokes vs. Background

Accompanying Polygon Annotation Files

21.186 Polygon Annotations

59 Object Classes

Scripts for Data Loading, Statistics, Consistency Check and Training Preparation

Dataset used to evaluate the method described in "Yeast cell detection and segmentation in bright field microscopy", ISBI 2014 (DOI: 10.1109/ISBI.2014.6868107). Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications: "Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32) "Hierarchical Planar Correlation Clustering for Cell Segmentation", EMMCVPR 2015. (DOI: 10.1007/978-3-319-14612-6_36) "Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)

This dataset contains 1660 images of electric substations with 50705 annotated objects. The images were obtained using different cameras, including cameras mounted on Autonomous Guided Vehicles (AGVs), fixed location cameras and those captured by humans using a variety of cameras. A total of 15 classes of objects were identified in this dataset, and the number of instances for each class is provided in the following table:

All images in this dataset were collected from a single electrical distribution substation in Brazil over a period of two years. The images were captured at various times of the day and under different weather and seasonal conditions, ensuring a diverse range of lighting conditions for the depicted objects. A team of experts in Electrical Engineering curated all the images to ensure that the angles and distances depicted in the images are suitable for automating inspections in an electrical substation.

The file structure of this dataset contains the following directories and files:

images: This directory contains 1660 electrical substation images in JPEG format.

images: This directory contains 1660 electrical substation images in JPEG format.

• labels_json: This directory contains JSON files annotated in the VOC-style polygonal format. Each file shares the same filename as its respective image in the images directory.
• 15_masks: This directory contains PNG segmentation masks for all 15 classes, including the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory.
• 14_masks: This directory contains PNG segmentation masks for all classes except the porcelain pin insulator. Each file shares the same name as its corresponding image in the images directory.
• porcelain_masks: This directory contains PNG segmentation masks for the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory.
• classes.txt: This text file lists the 15 classes plus the background class used in LabelMe.
• json2png.py: This Python script can be used to generate segmentation masks using the VOC-style polygonal JSON annotations.

The dataset aims to support the development of computer vision techniques and deep learning algorithms for automating the inspection process of electrical substations. The dataset is expected to be useful for researchers, practitioners, and engineers interested in developing and testing object detection and segmentation models for automating inspection and maintenance activities in electrical substations.

The authors would like to thank UTFPR for the support and infrastructure made available for the development of this research and COPEL-DIS for the support through project PD-2866-0528/2020—Development of a Methodology for Automatic Analysis of Thermal Images. We also would like to express our deepest appreciation to the team of annotators who worked diligently to produce the semantic labels for our dataset. Their hard work, dedication and attention to detail were critical to the success of this project.

This is Part 2/2 of the ActiveHuman dataset! Part 1 can be found here.

Dataset Description

ActiveHuman was generated using Unity's Perception package.

It consists of 175428 RGB images and their semantic segmentation counterparts taken at different environments, lighting conditions, camera distances and angles. In total, the dataset contains images for 8 environments, 33 humans, 4 lighting conditions, 7 camera distances (1m-4m) and 36 camera angles (0-360 at 10-degree intervals).

The dataset does not include images at every single combination of available camera distances and angles, since for some values the camera would collide with another object or go outside the confines of an environment. As a result, some combinations of camera distances and angles do not exist in the dataset.

Alongside each image, 2D Bounding Box, 3D Bounding Box and Keypoint ground truth annotations are also generated via the use of Labelers and are stored as a JSON-based dataset. These Labelers are scripts that are responsible for capturing ground truth annotations for each captured image or frame. Keypoint annotations follow the COCO format defined by the COCO keypoint annotation template offered in the perception package.

Folder configuration

The dataset consists of 3 folders:

• JSON Data: Contains all the generated JSON files.
• RGB Images: Contains the generated RGB images.
• Semantic Segmentation Images: Contains the generated semantic segmentation images.

Essential Terminology

• Annotation: Recorded data describing a single capture.
• Capture: One completed rendering process of a Unity sensor which stored the rendered result to data files (e.g. PNG, JPG, etc.).
• Ego: Object or person on which a collection of sensors is attached to (e.g., if a drone has a camera attached to it, the drone would be the ego and the camera would be the sensor).
• Ego coordinate system: Coordinates with respect to the ego.
• Global coordinate system: Coordinates with respect to the global origin in Unity.
• Sensor: Device that captures the dataset (in this instance the sensor is a camera).
• Sensor coordinate system: Coordinates with respect to the sensor.
• Sequence: Time-ordered series of captures. This is very useful for video capture where the time-order relationship of two captures is vital.
• UIID: Universal Unique Identifier. It is a unique hexadecimal identifier that can represent an individual instance of a capture, ego, sensor, annotation, labeled object or keypoint, or keypoint template.

Dataset Data

The dataset includes 4 types of JSON annotation files files:

• annotation_definitions.json: Contains annotation definitions for all of the active Labelers of the simulation stored in an array. Each entry consists of a collection of key-value pairs which describe a particular type of annotation and contain information about that specific annotation describing how its data should be mapped back to labels or objects in the scene. Each entry contains the following key-value pairs:
  • id: Integer identifier of the annotation's definition.
  • name: Annotation name (e.g., keypoints, bounding box, bounding box 3D, semantic segmentation).
  • description: Description of the annotation's specifications.
  • format: Format of the file containing the annotation specifications (e.g., json, PNG).
  • spec: Format-specific specifications for the annotation values generated by each Labeler.

Most Labelers generate different annotation specifications in the spec key-value pair:

• BoundingBox2DLabeler/BoundingBox3DLabeler:
  • label_id: Integer identifier of a label.
  • label_name: String identifier of a label.
• KeypointLabeler:
  • template_id: Keypoint template UUID.
  • template_name: Name of the keypoint template.
  • key_points: Array containing all the joints defined by the keypoint template. This array includes the key-value pairs:
    • label: Joint label.
    • index: Joint index.
    • color: RGBA values of the keypoint.
    • color_code: Hex color code of the keypoint
  • skeleton: Array containing all the skeleton connections defined by the keypoint template. Each skeleton connection defines a connection between two different joints. This array includes the key-value pairs:
    • label1: Label of the first joint.
    • label2: Label of the second joint.
    • joint1: Index of the first joint.
    • joint2: Index of the second joint.
    • color: RGBA values of the connection.
    • color_code: Hex color code of the connection.
• SemanticSegmentationLabeler:
  • label_name: String identifier of a label.
  • pixel_value: RGBA values of the label.
  • color_code: Hex color code of the label.

• captures_xyz.json: Each of these files contains an array of ground truth annotations generated by each active Labeler for each capture separately, as well as extra metadata that describe the state of each active sensor that is present in the scene. Each array entry in the contains the following key-value pairs:
  • id: UUID of the capture.
  • sequence_id: UUID of the sequence.
  • step: Index of the capture within a sequence.
  • timestamp: Timestamp (in ms) since the beginning of a sequence.
  • sensor: Properties of the sensor. This entry contains a collection with the following key-value pairs:
    • sensor_id: Sensor UUID.
    • ego_id: Ego UUID.
    • modality: Modality of the sensor (e.g., camera, radar).
    • translation: 3D vector that describes the sensor's position (in meters) with respect to the global coordinate system.
    • rotation: Quaternion variable that describes the sensor's orientation with respect to the ego coordinate system.
    • camera_intrinsic: matrix containing (if it exists) the camera's intrinsic calibration.
    • projection: Projection type used by the camera (e.g., orthographic, perspective).
  • ego: Attributes of the ego. This entry contains a collection with the following key-value pairs:
    • ego_id: Ego UUID.
    • translation: 3D vector that describes the ego's position (in meters) with respect to the global coordinate system.
    • rotation: Quaternion variable containing the ego's orientation.
    • velocity: 3D vector containing the ego's velocity (in meters per second).
    • acceleration: 3D vector containing the ego's acceleration (in ).
  • format: Format of the file captured by the sensor (e.g., PNG, JPG).
  • annotations: Key-value pair collections, one for each active Labeler. These key-value pairs are as follows:
    • id: Annotation UUID .
    • annotation_definition: Integer identifier of the annotation's definition.
    • filename: Name of the file generated by the Labeler. This entry is only present for Labelers that generate an image.
    • values: List of key-value pairs containing annotation data for the current Labeler.

Each Labeler generates different annotation specifications in the values key-value pair:

• BoundingBox2DLabeler:
  • label_id: Integer identifier of a label.
  • label_name: String identifier of a label.
  • instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values.
  • x: Position of the 2D bounding box on the X axis.
  • y: Position of the 2D bounding box position on the Y axis.
  • width: Width of the 2D bounding box.
  • height: Height of the 2D bounding box.
• BoundingBox3DLabeler:
  • label_id: Integer identifier of a label.
  • label_name: String identifier of a label.
  • instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values.
  • translation: 3D vector containing the location of the center of the 3D bounding box with respect to the sensor coordinate system (in meters).
  • size: 3D

This fish data is acquired from a live video dataset resulting in 27370 verified fish images. The whole dataset is divided into 23 clusters and each cluster is presented by a representative species, which is based on the synapomorphies characteristic from the extent that the taxon is monophyletic. The representative image indicates the distinction between clusters shown in the figure below, e.g. the presence or absence of components (anal-fin, nasal, infraorbitals), specific number (six dorsal-fin spines, two spiny dorsal-fins), particular shape (second dorsal-fin spine long), etc. This figure shows the representative fish species name and the numbers of detections. The data is very imbalanced where the most frequent species is about 1000 times more than the least one. The fish detection and tracking software described in [1] is used to obtain the fish images. The fish species are manually labeled by following instructions from marine biologists [2]. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5980358%2F5cc6093c54b3dc535bed661e93fc7a12%2Fgt_labels.png?generation=1700950637892694&alt=media" alt="">

Original page created by Phoenix X. Huang, Bastiaan B. Boom and Robert B. Fisher. Permission is granted for anyone to copy, use, modify, or distribute this data and accompanying documents for any purpose, provided this copyright notice is retained and prominently displayed, along with a note saying that the original data are available from our web page and refering to [2]. The data and documents are distributed without any warranty, express or implied. As the data were acquired for research purposes only, they have not been tested to the degree that would be advisable in any important application. All use of these data is entirely at the user's own risk.

Acknowledgments: This research was funded by European Commission FP7 grant 257024, in the Fish4Knowledge project.

• [1]. B. J. Boom, P. X. Huang, C. Spampinato, S. Palazzo, J. He, C. Beyan, E. Beauxis-Aussalet, J. van Ossenbruggen, G. Nadarajan, J. Y. Chen-Burger, D. Giordano, L. Hardman, F.-P. Lin, R. B. Fisher, "Long-term underwater camera surveillance for monitoring and analysis of fish populations", Proc. Int. Workshop on Visual observation and Analysis of Animal and Insect Behavior (VAIB), in conjunction with ICPR 2012, Tsukuba, Japan, 2012.

• [2]. B. J. Boom, P. X. Huang, J. He, R. B. Fisher, "Supporting Ground-Truth annotation of image datasets using clustering", 21st Int. Conf. on Pattern Recognition (ICPR), 2012.

Training, validation, and testing sets of DeTEXT.

Reference texts such as encyclopedias and news articles can manifest biased language when objective reporting is substituted by subjective writing. Existing methods to detect linguistic cues of bias mostly rely on annotated data to train machine learning models. However, low annotator agreement and comparability is a substantial drawback in available media bias corpora. To improve available datasets, we collect and compare labels obtained from two popular crowdsourcing platforms. Our results demonstrate the existing crowdsourcing approaches' lack of data quality, underlining the need for a trained expert framework to gather a more reliable dataset. Improving the agreement from Krippendorff's (\alpha) = 0.144 (crowdsourcing labels) to (\alpha) = 0.419 (expert labels), we assume that trained annotators' linguistic knowledge increases data quality improving the performance of existing bias detection systems.

The expert annotations are meant to be used to enrich the dataset MBIC – A Media Bias Annotation Dataset Including Annotator Characteristics available at https://zenodo.org/record/4474336#.YBHO6xYxmK8.

The annotation agreement of the 10 figures randomly selected.

YOLOv8 Image segmentation dataset: PELLET Casimir Marius

This dataset includes 100 images from the PELLET Casimir Marius story on Europeana. It is available in YOLOv8 format, to train a model to segment text lines and illustrations from page images. The ground truth was generated using Teklia's open-source annotation interface Callico. This work is marked with CC0 1.0. To view a copy of this license, visit https://creativecommons.org/publicdomain/zero/1.0/.