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 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 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.

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

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 thes...

Ground Truth

## Overview

Ground Truth is a dataset for object detection tasks - it contains Cow annotations for 828 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 images which have been rendered from the Blender Carpark model. Frames include a mixture of lighting scenes from bright to dark as well as three different weather conditions: sunny, wet and foggy. For each rendered frame a corresponding ground truth depth and semantic annotation was generated and added to the dataset.

Dataset

This dataset was created by Harrison Chapman

Contents

It contains the following files:

Ground Truth 3

## Overview

Ground Truth 3 is a dataset for object detection tasks - it contains Ship annotations for 458 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 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: 2.112 Raw Image Files 175.300 Bounding Box Annotations 229 Binary Segmentation Maps with accompanying Polygon Annotations Statistics, Consistency and Segmentation Workflow Scripts

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.

CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs). Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates; we manually choose the images to guarantee that each IP inside an ROI matches one of the following cloud cover groups:- clear (0%)- low-cloudy (1% - 25%) - almost clear (25% - 45%)- mid-cloudy (45% - 65%)- cloudy (65% >)An IP is the core unit in CloudSEN12. Each IP contains data from Sentinel-2 optical levels 1C and 2A, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from eight cutting-edge cloud detection algorithms. Besides, in order to support standard, weakly, and self-/semi-supervised learning procedures, cloudSEN12 includes three distinct forms of hand-crafted labelling data: high-quality, scribble, and no annotation. Consequently, each ROI is randomly assigned to a different annotation group:2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group).2000 ROIs with scribble-level annotation, where the annotation time is 15 minutes (scribble group).5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group).For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, combining human photo-interpretation and machine learning. For scribble, ground truth pixels were drawn using IRIS but without ML support. Finally, the no-annotation dataset is generated automatically, with manual annotation only in the clear image patch. A backup of the dataset in STAC format is available here: https://shorturl.at/cgjtz. Check out our website https://cloudsen12.github.io/ for examples.

Emotion analysis from app reviews - Replication packageFull paper accepted at the 33rd IEEE International Requirements Engineering 2025 conference (Research Track).📚 Summary of artifactThis artifact supports the replication of the study presented in the paper "What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews", accepted at the 33rd IEEE International Requirements Engineering 2025 conference. It provides a comprehensive framework for conducting fine-grained emotion analysis from mobile app reviews using both human and large language model (LLM)-based annotations.The artifact includes:Input: A dataset of user reviews, emotion annotation guidelines, and ground truth annotations from human annotators.Process: Scripts for generating emotion annotations via LLMs (GPT-4o, Mistral Large 2, and Gemini 2.0 Flash), splitting annotations into iterations, computing agreement metrics (e.g., Cohen’s Kappa), and evaluating correctness and cost-efficiency.Output: Annotated datasets (human and LLM-generated), agreement analyses, emotion statistics, and evaluation metrics including accuracy, precision, recall, and F1 score.The artifact was developed to ensure transparency, reproducibility, and extensibility of the experimental pipeline. It enables researchers to replicate, validate, or extend the emotion annotation process across different LLMs and configurations, contributing to the broader goal of integrating emotional insights into requirements engineering practices.🔎 Artifact LocationThe artifact is available at https://doi.org/10.6084/m9.figshare.28548638.Find how to cite this replication package and author information at the end of this README file.📂 Description of ArtifactLiterature review: results from the literature review on opinion mining and emotion analysis within the context of software-based reviews.Data: data used in the study, including user reviews (input), human annotations (ground truth), and LLM-based annotations (generated by the assistants).Code: code used in the study, including the generative annotation, data processing, and evaluation.📖 Literature reviewStudy selection and results are available in the literature_review/study-selection.xlsx file. This file contains the following sheets:iteration_1_IC_analysis: results from the first iteration of the inclusion criteria analysis.iteration_1_feature_extraction: results from the first iteration of the feature extraction analysis.iteration_2_IC_analysis: results from the second iteration of the inclusion criteria analysis.iteration_2_feature_extraction: results from the second iteration of the feature extraction analysis.iteration_3_IC_analysis: results from the third iteration of the inclusion criteria analysis.iteration_3_feature_extraction: results from the third iteration of the feature extraction analysis.emotions: statistical analysis of emotions covered by emotion taxonomies in the selected studies.🗃️ DataThe data root folder contains the following files:reviews.json contains the reviews used in the study.guidelines.txt contains a .txt version of the annotation guidelines.ground-truth.xlsx contains the ground truth (human agreement) annotations for the reviews.In addition, the data root folder contains the following subfolders:assistants contains the IDs of the assistants used for the generative annotation (see LLM-based annotation).annotations contains the results of the human and LLM-based annotation: -- iterations contains both human and LLM-based annotations for each iteration. -- llm-annotations contains the LLM-based annotations for each assistance, including results for various temperature values: low (0), medium (0.5), and high (1) (see LLM-based annotation).agreements contains the results of the agreement analysis between the human and LLM-based annotations (see Data Processing).evaluation contains the results of the evaluation of the LLM-based annotations (see Evaluation), including statistics, Cohen's Kappa, correctness, and cost-efficiency analysis, which includes token usage and human annotation reported times.⚙️ System RequirementsAll artifacts in this replication package are runnable in any operating system with the following requirements:OS: Linux Based OS // Mac-OS // Windows With Unix Like Shells For Example Git Bash CLIPython 3.10Additionally, you will also need at least one API key for OpenAI, Mistral or Gemini. See Step 1 in Usage Instructions & Steps to reproduce.💻 Installation Instructions⚙️ Install requirementsCreate a virtual environment:python -m venv venvActivate the virtual environment. For Linux Based OS Or Mac-OS.source venv/bin/activateFor Windows With Unix Like Shells (for example Git Bash CLI):source venv/Scripts/activateInstall Python dependency requirements running the following command.pip install -r requirements.txtNow you're ready to start the annotation process!💻 Usage Instructions & Steps to reproduceWe structure the code available in this replication package based on the stages involved in the LLM-based annotation process.🤖 LLM-based annotationThe llm_annotation folder contains the code used to generate the LLM-based annotations.There are two main scripts:create_assistant.py is used to create a new assistant with a particular provider and model. This class includes the definition of a common system prompt across all agents, using the data/guidelines.txt file as the basis.annotate_emotions.py is used to annotate a set of emotions using a previously created assistant. This script includes the assessment of the output format, as well as some common metrics for cost-efficiency analysis and output file generation.Our research includes an LLM-based annotation experimentation with 3 LLMs: GPT-4o, Mistral Large 2, and Gemini 2.0 Flash. To illustrate the usage of the code, in this README we refer to the code execution for generating annotations using GPT-4o. However, full code is provided for all LLMs.🔑 Step 1: Add your API keyIf you haven't done this already, add your API key to the .env file in the root folder. For instance, for OpenAI, you can add the following:OPENAI_API_KEY=sk-proj-...🛠️ Step 2: Create an assistantCreate an assistant using the create_assistant.py script. For instance, for GPT-4o, you can run the following command:python ./code/llm_annotation/create_assistant_openai.py --guidelines ./data/guidelines.txt --model gpt-4oThis will create an assistant loading the data/guidelines.txt file and using the GPT-4o model.📝 Step 3: Annotate emotionsAnnotate emotions using the annotate_emotions.py script. For instance, for GPT-4o, you can run the following command using a small subset of 100 reviews from the ground truth as an example:python ./code/llm_annotation/annotate_emotions_openai.py --input ./data/ground-truth-small.xlsx --output ./data/annotations/llm/temperature-00/ --batch_size 10 --model gpt-4o --temperature 0 --sleep_time 10For annotating the whole dataset, run the following command (IMPORTANT: this will take more than 60 minutes due to OpenAI, Mistral and Gemini consumption times!):python ./code/llm_annotation/annotate_emotions_openai.py --input ./data/ground-truth.xlsx --output ./data/annotations/llm/temperature-00/ --batch_size 10 --model gpt-4o --temperature 0 --sleep_time 10Parameters include:input: path to the input file containing the set of reviews to annotate (e.g., data/ground-truth.xlsx).output: path to the output folder where annotations will be saved (e.g., data/annotations/llm/temperature-00/).batch_size: number of reviews to annotate for each user request (e.g., 10).model: model to use for the annotation (e.g., gpt-4o).temperature: temperature for the model responses (e.g., 0).sleep_time: time to wait between batches, in seconds (e.g., 10).This will annotate the emotions using the assistant created in the previous step, creating a new file with the same format as in the data/ground-truth.xlsx file.🔄 Data processingIn this stage, we refactor all files into iterations and we consolidate the agreement between multiple annotators or LLM runs. These logic serves both for human and LLM annotations. Parameters can be updated to include more annotators or LLM runs.✂️ Step 4: Split annotations into iterationsWe split the annotations into iterations based on the number of annotators or LLM runs. For instance, for GPT-4o (run 0), we can run the following command:python code/data_processing/split_annotations.py --input_file data/annotations/llm/temperature-00/gpt-4o-0-annotations.xlsx --output_dir data/annotations/iterations/This facilitates the Kappa analysis and agreement in alignment with each human iteration.🤝 Step 5: Analyse agreementWe consolidate the agreement between multiple annotators or LLM runs. For instance, for GPT-4o, we can run the following command to use the run from Step 3 (run 0) and three additional annotations (run 1, 2, and 3) already available in the replication package (NOTE: we simplify the process to speed up the analysis and avoid delays in annotation):python code/evaluation/agreement.py --input-folder data/annotations/iterations/ --output-folder data/agreements/ --annotators gpt-4o-0 gpt-4o-1 gpt-4o-2 gpt-4o-3For replicating our original study, run the following:python code/evaluation/agreement.py --input-folder data/annotations/iterations/ --output-folder data/agreements/ --annotators gpt-4o-1 gpt-4o-2 gpt-4o-3📊 EvaluationAfter consolidating agreements, we can evaluate both the Cohen's Kappa agreement and correctness between the human and LLM-based annotations. Our code allows any combination of annotators and LLM runs.📈 Step 6: Emotion statisticsWe evaluate the statistics of the emotions in the annotations, including emotion frequency, distribution, and correlation between emotions. For instance, for GPT-4o and the example in this README file, we can run the following command:python code/evaluation/emotion_statistics.py --input-file

This is a multilingual ground truth dataset for training, evaluating and testing the LaSER (Language-Specific Event Recommendation) model. It contains language-specific relevance scores for event-centric click-through pairs according to the publicly available Clickstream dataset in German, French and Russian as well as the user study annotations conducted for evaluating the language-specific recommendations by LaSER. For more details, refer to EventKG+Click and LaSER. This dataset consists of two sets of files as follows: 1. The ground truth dataset that is used for training the learning to rank (LTR) model in LaSER in three languages. The following files contain the language-specific relevance scores between a source and target entity based on EventKG+Click dataset: german_ground_truth.txt french_ground_truth.txt russian_ground_truth.txt In these files source and target represent the label of entities and events in the respective language. 2. The second set contains the user study participants' annotations regarding different relevance criteria of recommended events by LaSER. The following three files contain the annotations of at least three participants per event: german_user_study_annotations.csv french_user_study_annotations.csv russian_user_study_annotations.csv In these files, "r1", "r2" and "r3" denote relevance to the topic, language community and general audience respectively. And topic and event represent the wikidata-id of entities and events.

This dataset contains 7539 images of electric substations with 213566 annotated objects. The images were obtained using different cameras, ranging from cellphone cameras, panoramic aerial cameras, and stereo FLIR cameras, including cameras mounted on an Autonomous Guided Vehicles (AGV). A total of 15 classes of objects were identified in this dataset. These are the classes and their number of instances:Open blade disconnect switch: 1117Closed blade disconnect switch: 26068Open tandem disconnect switch: 4263Closed tandem disconnect switch: 5402Breaker: 4803Fuse disconnect switch: 1925Glass disc insulator: 13803Porcelain pin insulator: 114578Muffle: 8128Lightning arrester: 8788Recloser: 8059Power transformer: 2391Current transformer: 9293Potential transformer: 2904Tripolar disconnect switch: 2044All 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:misc: This directory contains 4030 images collected manually during the morning period, ranging from 8h00 up to 12h00;misc/labels: This subdirectory contains the 4030 YOLO annotated .txt files for the misc directory;agv_day: This directory contains 2270 images collected using an automated guided vehicle during daytime, ranging between 8h00 and 10h00 during morning and in the afternoon between 13h00 and 17h00;agv_day/labels: This subdirectory contains the 2270 YOLO annotated .txt files for the agv_day directory;agv_night_light: This directory contains 899 images collected using an automated guided vehicle during nighttime, where artificial lighting is present, ranging from 20h00 up to 21h00;agv_night_light/labels: This subdirectory contains the 899 YOLO annotated .txt files for the agv_night_light directory;agv_night_dark: This directory contains 340 images collected using an automated guided vehicle during nighttime, without artificial light sources, ranging from 20h00 up to 21h00;agv_night_dark/labels: This subdirectory contains the 340 YOLO annotated .txt files for the agv_night_dark directory;classes.txt: This text file lists the 15 classes indexed in the order used in the annotations.The dataset aims to support the development of computer vision techniques and deep learning algorithms for automating the inspection process of electrical substations. We expect it to be useful for researchers, practitioners, and engineers interested in developing and testing object detection models for automating inspection and maintenance activities in electrical substations.

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:

Object classes and how many times they appear in the dataset.


    Class
    Instances




    Open blade disconnect
    310


    Closed blade disconnect switch
    5243


    Open tandem disconnect switch
    1599


    Closed tandem disconnect switch
    966


    Breaker
    980


    Fuse disconnect switch
    355


    Glass disc insulator
    3185


    Porcelain pin insulator
    26499


    Muffle
    1354


    Lightning arrester
    1976


    Recloser
    2331


    Power transformer
    768


    Current transformer
    2136


    Potential transformer
    654


    Tripolar disconnect switch
    2349

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.

PersonPath22 is a large-scale multi-person tracking dataset containing 236 videos captured mostly from static-mounted cameras, collected from sources where we were given the rights to redistribute the content and participants have given explicit consent. Each video has ground-truth annotations including both bounding boxes and tracklet-ids for all the persons in each frame.

A Public Ground-Truth Dataset for Handwritten Circuit Diagrams (CGHD)

This repository contains images of hand-drawn electrical circuit diagrams as well as accompanying bounding box annotation, polygon annotation and segmentation files. These annotations serve as ground truth to train and evaluate several image processing tasks like object detection, instance segmentation and text detection. The purpose of this dataset is to facilitate the automated extraction of electrical graph… See the full description on the dataset page: https://huggingface.co/datasets/lowercaseonly/cghd.