The open-source data annotation tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market's expansion is fueled by several key factors: the rising adoption of AI across various industries (including automotive, healthcare, and finance), the need for efficient and cost-effective data annotation solutions, and a growing preference for flexible, customizable tools offered by open-source platforms. While cloud-based solutions currently dominate the market due to scalability and accessibility, on-premise deployments remain significant for organizations with stringent data security requirements. The competitive landscape is dynamic, with numerous established players and emerging startups vying for market share. The market is segmented geographically, with North America and Europe currently holding the largest shares due to early adoption of AI technologies and robust research & development activities. However, the Asia-Pacific region is projected to witness significant growth in the coming years, driven by increasing investments in AI infrastructure and talent development. Challenges remain, such as the need for skilled annotators and the ongoing evolution of annotation techniques to handle increasingly complex data types. The forecast period (2025-2033) suggests continued expansion, with a projected Compound Annual Growth Rate (CAGR) – let's conservatively estimate this at 15% based on typical growth in related software sectors. This growth will be influenced by advancements in automation and semi-automated annotation tools, as well as the emergence of novel annotation paradigms. The market is expected to see further consolidation, with larger players potentially acquiring smaller, specialized companies. The increasing focus on data privacy and security will necessitate the development of more robust and compliant open-source annotation tools. Specific application segments like healthcare, with its stringent regulatory landscape, and the automotive industry, with its reliance on autonomous driving technology, will continue to be major drivers of market growth. The increasing availability of open-source datasets and pre-trained models will indirectly contribute to the market’s expansion by lowering the barrier to entry for AI development.

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in various AI applications. The market's expansion is fueled by several key factors: the rising adoption of machine learning and deep learning algorithms across industries, the need for efficient and cost-effective data annotation solutions, and a growing preference for customizable and flexible tools that can adapt to diverse data types and project requirements. While proprietary solutions exist, the open-source ecosystem offers advantages including community support, transparency, cost-effectiveness, and the ability to tailor tools to specific needs, fostering innovation and accessibility. The market is segmented by tool type (image, text, video, audio), deployment model (cloud, on-premise), and industry (automotive, healthcare, finance). We project a market size of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of 25% from 2025 to 2033, reaching approximately $2.7 billion by 2033. This growth is tempered by challenges such as the complexities associated with data security, the need for skilled personnel to manage and use these tools effectively, and the inherent limitations of certain open-source solutions compared to their commercial counterparts. Despite these restraints, the open-source model's inherent flexibility and cost advantages will continue to attract a significant user base. The market's competitive landscape includes established players like Alecion and Appen, alongside numerous smaller companies and open-source communities actively contributing to the development and improvement of these tools. Geographical expansion is expected across North America, Europe, and Asia-Pacific, with the latter projected to witness significant growth due to the increasing adoption of AI and machine learning in developing economies. Future market trends point towards increased integration of automated labeling techniques within open-source tools, enhanced collaborative features to improve efficiency, and further specialization to cater to specific data types and industry-specific requirements. Continuous innovation and community contributions will remain crucial drivers of growth in this dynamic market segment.

Open Source Data Labelling Tool Market Outlook

The global market size for Open Source Data Labelling Tools was valued at USD 1.5 billion in 2023 and is projected to reach USD 4.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.2% during the forecast period. This significant growth can be attributed to the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries, which drives the need for accurately labelled data to train these technologies effectively.

The rapid advancement and integration of AI and ML in numerous sectors serve as a primary growth factor for the Open Source Data Labelling Tool market. With the proliferation of big data, organizations are increasingly recognizing the importance of high-quality, annotated data sets to enhance the accuracy and efficiency of their AI models. The open-source nature of these tools offers flexibility and cost-effectiveness, making them an attractive choice for businesses of all sizes, especially startups and SMEs, which further fuels market growth.

Another key driver is the rising demand for automated data labelling solutions. Manual data labelling is a time-consuming and error-prone task, leading many organizations to seek automated tools that can swiftly and accurately label large datasets. Open source data labelling tools, often augmented with advanced features like natural language processing (NLP) and computer vision, provide a scalable solution to this challenge. This trend is particularly pronounced in data-intensive industries such as healthcare, automotive, and finance, where the precision of data labelling can significantly impact operational outcomes.

Additionally, the collaborative nature of open-source communities contributes to the market's growth. Continuous improvements and updates are driven by a global community of developers and researchers, ensuring that these tools remain at the cutting edge of technology. This ongoing innovation not only boosts the functionality and reliability of open-source data labelling tools but also fosters a sense of community and shared knowledge, encouraging more organizations to adopt these solutions.

In the realm of data labelling, Premium Annotation Tools have emerged as a significant player, offering advanced features that cater to the needs of enterprises seeking high-quality data annotation. These tools often come equipped with enhanced functionalities such as collaborative interfaces, real-time updates, and integration capabilities with existing AI systems. The premium nature of these tools ensures that they are designed to handle complex datasets with precision, thereby reducing the margin of error in data labelling processes. As businesses increasingly prioritize accuracy and efficiency, the demand for premium solutions is on the rise, providing a competitive edge in sectors where data quality is paramount.

From a regional perspective, North America holds a significant share of the market due to the robust presence of tech giants and a well-established IT infrastructure. The region's strong focus on AI research and development, coupled with substantial investments in technology, drives the demand for data labelling tools. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, attributed to the rapid digital transformation and increasing AI adoption across countries like China, India, and Japan.

Component Analysis

When dissecting the Open Source Data Labelling Tool market by component, it is evident that the segment is bifurcated into software and services. The software segment dominates the market, primarily due to the extensive range of features and functionalities that open-source data labelling software offers. These tools are customizable and can be tailored to meet specific needs, making them highly versatile and efficient. The software segment is expected to continue its dominance as more organizations seek comprehensive solutions that integrate seamlessly with their existing systems.

The services segment, while smaller in comparison, plays a crucial role in the overall market landscape. Services include support, training, and consulting, which are vital for organizations to effectively implement and utilize open-source data labelling tools. As the adoption of these tools grows, so does the demand for professional services that can aid in deployment, customization

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in machine learning and artificial intelligence applications. The market's expansion is fueled by several factors: the rising adoption of AI across various sectors (including IT, automotive, healthcare, and finance), the need for cost-effective data annotation solutions, and the inherent flexibility and customization offered by open-source tools. While cloud-based solutions currently dominate the market due to scalability and accessibility, on-premise deployments remain significant, particularly for organizations with stringent data security requirements. The market's growth is further propelled by advancements in automation and semi-supervised learning techniques within data labeling, leading to increased efficiency and reduced annotation costs. Geographic distribution shows a strong concentration in North America and Europe, reflecting the higher adoption of AI technologies in these regions; however, Asia-Pacific is emerging as a rapidly growing market due to increasing investment in AI and the availability of a large workforce for data annotation. Despite the promising outlook, certain challenges restrain market growth. The complexity of implementing and maintaining open-source tools, along with the need for specialized technical expertise, can pose barriers to entry for smaller organizations. Furthermore, the quality control and data governance aspects of open-source annotation require careful consideration. The potential for data bias and the need for robust validation processes necessitate a strategic approach to ensure data accuracy and reliability. Competition is intensifying with both established and emerging players vying for market share, forcing companies to focus on differentiation through innovation and specialized functionalities within their tools. The market is anticipated to maintain a healthy growth trajectory in the coming years, with increasing adoption across diverse sectors and geographical regions. The continued advancements in automation and the growing emphasis on data quality will be key drivers of future market expansion.

To develop robust algorithms for automated diagnosis of medical conditions such as cardiac arrhythmias, researchers require large collections of data with human expert annotations. Currently, there is a lack of accessible, open-source platforms for human experts to collaboratively develop these annotated datasets through a web interface. In this work, we developed a flexible, generalizable, web-based framework to enable multiple users to create and share annotations on multi-channel physiological waveforms. The software is simple to install and offers a range of features, including: user management and task customization; a programmatic interface for data import and export; and a leaderboard for annotation progress tracking.

Image Annotation Tool Market Outlook

The global image annotation tool market size is projected to grow from approximately $700 million in 2023 to an estimated $2.5 billion by 2032, exhibiting a remarkable compound annual growth rate (CAGR) of 15.2% over the forecast period. The surging demand for machine learning and artificial intelligence applications is driving this robust market expansion. Image annotation tools are crucial for training AI models to recognize and interpret images, a necessity across diverse industries.

One of the key growth factors fueling the image annotation tool market is the rapid adoption of AI and machine learning technologies across various sectors. Organizations in healthcare, automotive, retail, and many other industries are increasingly leveraging AI to enhance operational efficiency, improve customer experiences, and drive innovation. Accurate image annotation is essential for developing sophisticated AI models, thereby boosting the demand for these tools. Additionally, the proliferation of big data analytics and the growing necessity to manage large volumes of unstructured data have amplified the need for efficient image annotation solutions.

Another significant driver is the increasing use of autonomous systems and applications. In the automotive industry, for instance, the development of autonomous vehicles relies heavily on annotated images to train algorithms for object detection, lane discipline, and navigation. Similarly, in the healthcare sector, annotated medical images are indispensable for developing diagnostic tools and treatment planning systems powered by AI. This widespread application of image annotation tools in the development of autonomous systems is a critical factor propelling market growth.

The rise of e-commerce and the digital retail landscape has also spurred demand for image annotation tools. Retailers are using these tools to optimize visual search features, personalize shopping experiences, and enhance inventory management through automated recognition of products and categories. Furthermore, advancements in computer vision technology have expanded the capabilities of image annotation tools, making them more accurate and efficient, which in turn encourages their adoption across various industries.

Data Annotation Software plays a pivotal role in the image annotation tool market by providing the necessary infrastructure for labeling and categorizing images efficiently. These software solutions are designed to handle various annotation tasks, from simple bounding boxes to complex semantic segmentation, enabling organizations to generate high-quality training datasets for AI models. The continuous advancements in data annotation software, including the integration of machine learning algorithms for automated labeling, have significantly enhanced the accuracy and speed of the annotation process. As the demand for AI-driven applications grows, the reliance on robust data annotation software becomes increasingly critical, supporting the development of sophisticated models across industries.

Regionally, North America holds the largest share of the image annotation tool market, driven by significant investments in AI and machine learning technologies and the presence of leading technology companies. Europe follows, with strong growth supported by government initiatives promoting AI research and development. The Asia Pacific region presents substantial growth opportunities due to the rapid digital transformation in emerging economies and increasing investments in technology infrastructure. Latin America and the Middle East & Africa are also expected to witness steady growth, albeit at a slower pace, due to the gradual adoption of advanced technologies.

Component Analysis

The image annotation tool market by component is segmented into software and services. The software segment dominates the market, encompassing a variety of tools designed for different annotation tasks, from simple image labeling to complex polygonal, semantic, or instance segmentation. The continuous evolution of software platforms, integrating advanced features such as automated annotation and machine learning algorithms, has significantly enhanced the accuracy and efficiency of image annotations. Furthermore, the availability of open-source annotation tools has lowered the entry barrier, allowing more organizations to adopt these technologies.

Services associated with image ann

The Open Source Data Annotation Tool market is rapidly evolving as businesses and researchers increasingly recognize the significance of high-quality, labeled data for training machine learning models. These tools facilitate the efficient tagging and classification of various data types, including images, text, and

a Traffic data Visualization and Annotation tool - version 1.1 as published in the Journal of Open-Source Software

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

This dataset was recorded in the Virtual Annotated Cooking Environment (VACE), a new open-source virtual reality dataset (https://sites.google.com/view/vacedataset) and simulator (https://github.com/michaelkoller/vacesimulator) for object interaction tasks in a rich kitchen environment. We use the Unity-based VR simulator to create thoroughly annotated video sequences of a virtual human avatar performing food preparation activities. Based on the MPII Cooking 2 dataset, it enables the recreation of recipes for meals such as sandwiches, pizzas, fruit salads and smaller activity sequences such as cutting vegetables. For complex recipes, multiple samples are present, following different orderings of valid partially ordered plans. The dataset includes an RGB and depth camera view, bounding boxes, object masks segmentation, human joint poses and object poses, as well as ground truth interaction data in the form of temporally labeled semantic predicates (holding, on, in, colliding, moving, cutting). In our effort to make the simulator accessible as an open-source tool, researchers are able to expand the setting and annotation to create additional data samples.

The research leading to these results has received funding from the Austrian Science Fund (FWF) under grant agreement No. I3969-N30 InDex and the project Doctorate College TrustRobots by TU Wien. Thanks go out to Simon Schreiberhuber for sharing his Unity expertise and to the colleagues at the TU Wien Center for Research Data Management for data hosting and support.

The Open Source Data Labelling Tool market has emerged as a crucial segment in the artificial intelligence and machine learning landscape, facilitating the efficient annotation of data for various applications. As organizations strive to develop more effective AI models, they increasingly rely on open-source solutio

In recent years, proprietary and open-source bioinformatics software tools have been developed for the identification of lipids in complex biological samples based on high-resolution mass spectrometry data. These existent software tools often rely on publicly available lipid databases, such as LIPID MAPS, which, in some cases, only contain a limited number of lipid species for a specific lipid class. Other software solutions implement their own lipid species databases, which are often confined regarding implemented lipid classes, such as phospholipids. To address these drawbacks, we provide an extension of the widely used open-source metabolomics software MZmine 2, which enables the annotation of detected chromatographic features as lipid species. The extension is designed for straightforward generation of a custom database for selected lipid classes. Furthermore, each lipid’s sum formula of the created database can be rapidly modified to search for derivatization products, oxidation products, in-source fragments, or adducts. The versatility will be exemplified by a liquid chromatography–high resolution mass spectrometry data set with postcolumn Paternò–Büchi derivatization. The derivatization reaction was performed to pinpoint the double bond positions in diacylglyceryltrimethylhomoserine lipid species in a lipid extract of a green algae (Chlamydomonas reinhardtii) sample. The developed Lipid Search module extension of MZmine 2 supports the identification of lipids as far as double bond position level.

Web based data management system that allows users to share, annotate, organize and analyze large collections of datasets. Software tool for creating some metadata about software tools automatically. Open source data management for long tail data. Provides support for extensible metadata annotation and distributed analytics for automatic curation of uploaded data. Open source software that can be customized and deployed on your own cloud.

We provide annotated fish imagery data for use in deep learning models (e.g., convolutional neural networks) for individual and species recognition. For individual recognition models, the dataset consists of annotated .json files of individual brook trout imagery collected at the Eastern Ecological Science Center's Experimental Stream Laboratory. For species recognition models, the dataset consists of annotated .json files for 7 freshwater fish species: lake trout, largemouth bass, smallmouth bass, brook trout, rainbow trout, walleye, and northern pike. Species imagery was compiled from Anglers Atlas and modified to remove human faces for privacy protection. We used open-source VGG image annotation software developed by Oxford University: https://www.robots.ox.ac.uk/~vgg/software/via/via-1.0.6.html.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

• The dataset is gathered on Sep. 17th 2020. It has more than 5.4K Python repositories that are hosted on GitHub. Check out the file ManyTypes4PyDataset.spec for repositories URL and their commit SHA.
• The dataset is also de-duplicated using the CD4Py tool. The list of duplicate files is provided in duplicate_files.txt file.
• All of its Python projects are processed in JSON-formatted files. They contain a seq2seq representation of each file, type-related hints, and information for machine learning models. The structure of JSON-formatted files is described in JSONOutput.md file.
• The dataset is split into train, validation and test sets by source code files. The list of files and their corresponding set is provided in dataset_split.csv file.
• Notable changes to each version of the dataset are documented in CHANGELOG.md.

AdA Project Public Data Release

This repository holds public data provided by the AdA project (Affektrhetoriken des Audiovisuellen - BMBF eHumanities Research Group Audio-Visual Rhetorics of Affect).

See: http://www.ada.cinepoetics.fu-berlin.de/en/index.html The data is made accessible under the terms of the Creative Commons Attribution-ShareAlike 3.0 License. The data can be accessed also at the project's public GitHub repository: https://github.com/ProjectAdA/public

Further explanations of the data can be found on our AdA project website: https://projectada.github.io/. See also the peer-reviewed data paper for this dataset that is in review to be published in NECSUS_European Journal of Media Studies, and will be available from https://necsus-ejms.org/ and https://mediarep.org

The data currently includes:

AdA Filmontology

The latest public release of the AdA Filmontology: https://github.com/ProjectAdA/public/tree/master/ontology

A vocabulary of film-analytical terms and concepts for fine-grained semantic video annotation.

The vocabulary is also available online in our triplestore: https://ada.cinepoetics.org/resource/2021/05/19/eMAEXannotationMethod.html

Advene Annotation Template

The latest public release of the template for the Advene annotation software: https://github.com/ProjectAdA/public/tree/master/advene_template

The template provides the developed semantic vocabulary in the Advene software with ready-to-use annotation tracks and predefined values.

In order to use the template you have to install and use Advene: https://www.advene.org/

Annotation Data

The latest public releases of our annotation datasets based on the AdA vocabulary: https://github.com/ProjectAdA/public/tree/master/annotations

The dataset of news reports, documentaries and feature films on the topic of "financial crisis" contains more than 92.000 manual & semi-automatic annotations authored in the open-source-software Advene (Aubert/Prié 2005) by expert annotators as well as more than 400.000 automatically generated annotations for wider corpus exploration. The annotations are published as Linked Open Data under the CC BY-SA 3.0 licence and available as rdf triples in turtle exports (ttl files) and in Advene's non-proprietary azp-file format, which allows instant access through the graphical interface of the software.

The annotation data can also be queried at our public SPARQL Endpoint: http://ada.filmontology.org/sparql

• The dataset contains furthermore sample files for two different export capabilities of the web application AdA Annotation explorer: 1) all manual annotations of the type "field size" throughout the corpus as csv files. 2) static html exports of different queries conducted in the AdA Annotation Explorer.

Manuals

The data set includes different manuals and documentations in German and English: https://github.com/ProjectAdA/public/tree/master/manuals

"AdA Filmontology – Levels, Types, Values": an overview over all analytical concepts and their definitions.

"Manual: Annotating with Advene and the AdA Filmontology". A manual on the usage of Advene and the AdA Annotation Explorer that provides the basics for annotating audiovisual aesthetics and visualizing them.

"Notes on collaborative annotation with the AdA Filmontology"

Introduction:

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🌟 XimageNet-12 🌟

An Explainable Visual Benchmark Dataset for Robustness Evaluation. A Dataset for Image Background Exploration!

Blur Background, Segmented Background, AI-generated Background, Bias of Tools During Annotation, Color in Background, Random Background with Real Environment

+⭐ Follow Authors for project updates.

Website: XimageNet-12

Here, we trying to understand how image background effect the Computer Vision ML model, on topics such as Detection and Classification, based on baseline Li et.al work on ICLR 2022: Explainable AI: Object Recognition With Help From Background, we are now trying to enlarge the dataset, and analysis the following topics: Blur Background / Segmented Background / AI generated Background/ Bias of tools during annotation/ Color in Background / Dependent Factor in Background/ LatenSpace Distance of Foreground/ Random Background with Real Environment! Ultimately, we also define the math equation of Robustness Scores! So if you feel interested How would we make it or join this research project? please feel free to collaborate with us!

In this paper, we propose an explainable visual dataset, XIMAGENET-12, to evaluate the robustness of visual models. XIMAGENET-12 consists of over 200K images with 15,410 manual semantic annotations. Specifically, we deliberately selected 12 categories from ImageNet, representing objects commonly encountered in practical life. To simulate real-world situations, we incorporated six diverse scenarios, such as overexposure, blurring, and color changes, etc. We further develop a quantitative criterion for robustness assessment, allowing for a nuanced understanding of how visual models perform under varying conditions, notably in relation to the background.

Progress:

• Blur Background-> Done! You can find the image Generated in the corresponding folder!
• Segmented Background -> Done! you can download the image and its corresponding transparent mask image!
• Color in Background->Done!~~ you can now download the image with different background color modified, and play with different color-ed images!
• Random Background with Real Environment -> Done! you can also find we generated the image with the photographer's real image as a background and removed the original background of the target object, but similar to the style!
• Bias of tools during annotation->Done! for this one, you won't get a new image, because this is about math and statistics data analysis when different tools and annotators are applied!
• AI generated Background-> current on progress ( 12 /12) Done!, So basically you can find one sample folder image we uploaded, please take a look at how real it is, and guess what LLM model we are using to generate the high-resolution background to make it so real :)

What tool we used to generate those images?

We employed a combination of tools and methodologies to generate the images in this dataset, ensuring both efficiency and quality in the annotation and synthesis processes.

• IoG Net: Initially, we utilized the IoG Net, which played a foundational role in our image generation pipeline.
• Polygon Faster Labeling Tool: To facilitate the annotation process, we developed a custom Polygon Faster Labeling Tool, streamlining the labeling of objects within the images.AnyLabeling Open-source Project: We also experimented with the AnyLabeling open-source project, exploring its potential for our annotation needs.
• V7 Lab Tool: Eventually, we found that the V7 Lab Tool provided the most efficient labeling speed and delivered high-quality annotations. As a result, we standardized the annotation process using this tool.
• Data Augmentation: For the synthesis of synthetic images, we relied on a combination of deep learning frameworks, including scikit-learn and OpenCV. These tools allowed us to augment and manipulate images effectively to create a diverse range of backgrounds and variations.
• GenAI: Our dataset includes images generated using the Stable Diffusion XL model, along with versions 1.5 and 2.0 of the Stable Diffusion model. These generative models played a pivotal role in crafting realistic and varied backgrounds.

For a detailed breakdown of our prompt engineering and hyperparameters, we invite you to consult our upcoming paper. This publication will provide comprehensive insights into our methodologies, enabling a deeper understanding of the image generation process.

How to use our dataset?

this dataset has been/could be downloaded via Kaggl...