Competition

• Generate the BEST tabular synthetic data and win 100,000 USD in cash.
• Competition runs for 50 days: May 14 - July 3, 2025.
• MOSTLY AI Prize

This competition features two independent synthetic data challenges that you can join separately: - The FLAT DATA Challenge - The SEQUENTIAL DATA Challenge

For each challenge, generate a dataset with the same size and structure as the original, capturing its statistical patterns — but without being significantly closer to the (released) original samples than to the (unreleased) holdout samples.

Train a generative model that generalizes well, using any open-source tools (Synthetic Data SDK, synthcity, reprosyn, etc.) or your own solution. Submissions must be fully open-source, reproducible, and runnable within 6 hours on a standard machine.

Timeline

• Submissions open: May 14, 2025, 15:30 UTC
• Submission credits: 3 per calendar week (+bonus)
• Submissions close: July 3, 2025, 23:59 UTC
• Evaluation of Leaders: July 3 - July 9
• Winners announced: on July 9 🏆

Datasets

Flat Data - 100,000 records - 80 data columns: 60 numeric, 20 categorical

Sequential Data - 20,000 groups - each group contains 5-10 records - 10 data columns: 7 numeric, 3 categorical

Evaluation

• CSV submissions are parsed using pandas.read_csv() and checked for expected structure & size
• Evaluated using the Synthetic Data Quality Assurance toolkit
• Compared against the released training set and a hidden holdout set (same size, non-overlapping, from the same source)

Submission

MOSTLY AI Prize

Citation

If you use this dataset in your research, please cite:

@dataset{mostlyaiprize,
 author = {MOSTLY AI},
 title = {MOSTLY AI Prize Dataset},
 year = {2025},
 url = {https://www.mostlyaiprize.com/},
}

Synthetic Corpora Expansion Market Outlook

According to our latest research, the global synthetic corpora expansion market size reached USD 1.38 billion in 2024, reflecting robust momentum in the adoption of artificial data generation technologies. The market is projected to grow at a CAGR of 29.5% from 2025 to 2033, resulting in a forecasted value of USD 13.55 billion by 2033. This impressive growth trajectory is primarily driven by the escalating demand for high-quality, diverse, and scalable datasets to power advanced artificial intelligence (AI) and machine learning (ML) models across various industries.

One of the primary growth factors propelling the synthetic corpora expansion market is the increasing reliance on AI-driven applications that require vast and varied datasets for effective training. As organizations strive to enhance the accuracy and reliability of their AI models, the limitations of real-world data—such as privacy concerns, scarcity, and labeling costs—have become more pronounced. Synthetic corpora offer a viable solution by generating artificial datasets that mimic real-world data distributions while addressing issues of data privacy and accessibility. This capability is especially critical in regulated sectors like healthcare and finance, where data sensitivity and compliance requirements are paramount. The scalability and flexibility of synthetic data generation tools further support rapid experimentation and model iteration, fueling widespread adoption in research and enterprise environments alike.

Another significant driver for the synthetic corpora expansion market is the rapid evolution of natural language processing (NLP), speech recognition, and machine translation technologies. These applications rely heavily on large volumes of annotated data, which are often difficult and expensive to obtain in sufficient quantities. Synthetic corpora enable organizations to augment their existing datasets, improve model generalization, and reduce the risk of bias by introducing controlled variations and rare linguistic patterns. The integration of synthetic data generation into AI development pipelines also accelerates time-to-market for innovative solutions, as it minimizes dependency on manual data collection and annotation. As the sophistication of generative models continues to advance, the quality and utility of synthetic corpora are expected to improve, further expanding their role in AI research and deployment.

The growing emphasis on data augmentation and the democratization of AI technologies are also contributing to market expansion. Startups, academic institutions, and enterprises of all sizes are leveraging synthetic corpora to overcome data scarcity and enhance the robustness of their AI models. The proliferation of open-source frameworks, cloud-based platforms, and commercial synthetic data generation services has lowered the barrier to entry, enabling a broader range of organizations to experiment with and benefit from synthetic corpora. This trend is particularly evident in emerging markets, where access to large-scale real-world datasets may be limited. As regulatory scrutiny around data privacy intensifies, the adoption of synthetic corpora is poised to become a strategic imperative for organizations seeking to innovate responsibly and maintain a competitive edge.

Regionally, North America remains the dominant force in the synthetic corpora expansion market, accounting for the largest share of global revenue in 2024. The region's leadership is underpinned by a mature AI ecosystem, significant investments in research and development, and a high concentration of technology giants and startups. Europe and Asia Pacific are also witnessing rapid growth, driven by increasing digital transformation initiatives, supportive government policies, and a burgeoning talent pool in data science and AI. While Latin America and the Middle East & Africa currently represent smaller market shares, these regions are expected to post above-average growth rates over the forecast period as local industries embrace AI-driven innovation and synthetic data solutions.

Component Analysis

The synthetic corpora expansion market is segmented by component into software, services, and platforms. The software segment holds a significant share of the market, driven by the continuous development of advanced tools for synthetic data generation, annotation, and validation. These software solutions are designed to c

Synthetic Legal Contract Dataset — Powered by Syncora

High-Fidelity Synthetic Dataset for LLM Training, Legal NLP & AI Research

About This Dataset

This repository provides a synthetic dataset of legal contract Q&A interactions, modeled after real-world corporate filings (e.g., SEC 8-K disclosures). The data is generated using Syncora.ai, ensuring privacy-safe, fake data that is safe to use in LLM training, benchmarking, and experimentation.

This free dataset captures the style and structure of legal exchanges without exposing any confidential or sensitive client information.

Dataset Context & Features

🚨 Simulated Regulatory Scenarios

This synthetic legal dataset is not just for LLM training — it enables developers and researchers to create simulated regulatory scenarios. Examples include:

• Detecting high-risk clauses in contracts before real-world deployment
• Testing AI models on rare or edge-case compliance situations
• Simulating SEC filings and corporate disclosures to evaluate NLP models
• Benchmarking contract analysis tools safely without exposing sensitive data

This section gives the dataset a practical, standout value, showing it can be used for stress-testing AI in legal environments.

Why Syncora?

Syncora.ai creates synthetic datasets optimized for LLM training with:

• High similarity to real-world distributions
• Free dataset access for research and open innovation
• 0% privacy leakage — fully synthetic fake data
• Robust benchmarking potential for AI & legal NLP tasks

🔗 Generate Your Own Synthetic Data

Take your AI projects further with Syncora.ai:
→ Generate your own synthetic datasets now

📜 License

This dataset is released under the MIT License.

It is 100% synthetic, safe for LLM training, and ideal for research, experimentation, and open-source projects.

Medical Education Curriculum Dataset by Deepfabric

  Dataset Description

This synthetic dataset contains 7,570 high-quality conversations focused on medical education curriculum design and clinical training. The conversations simulate realistic discussions between medical curriculum committee chairs, educators, and healthcare professionals designing comprehensive learning pathways. It was produced using the Open Source Synthetic dataset generation tool, DeepFabric If you… See the full description on the dataset page: https://huggingface.co/datasets/alwaysfurther/deepfabric-7k-medical-multi-turn-conversation.

Synthetic Text Image Dataset for Arabic and English OCR Overview This dataset provides 200,000 synthetic text images (100,000 Arabic, 100,000 English) designed for training and evaluating Optical Character Recognition (OCR) models. Generated using a customized version of the TextRecognitionDataGenerator tool, it features diverse fonts, sizes, colors, and backgrounds to simulate real-world text recognition scenarios. The text content is sourced from the Arabic News Summarization dataset, augmented with random digit-heavy strings (e.g., phone numbers, dates) for enhanced variety. Ideal for multilingual OCR research, this dataset supports applications in recognizing mixed Arabic (right-to-left) and English text, including letters, digits, punctuation, and symbols. It is suitable for training, fine-tuning, and evaluating OCR models, particularly for challenging scripts and mixed-language contexts. Dataset Structure The dataset is organized into two main directories:

ar/: Contains 100,000 synthetic Arabic text images. labels.txt: Maps image filenames to their text in the format image_name text_in_image. xxxxxx.jpg: JPEG images containing Arabic text.

en/: Contains 100,000 synthetic English text images. labels.txt: Maps image filenames to their text in the format image_name text_in_image. xxxxxx.jpg: JPEG images containing English text.

Features

Text Content: Arabic and English text from the Arabic News Summarization dataset. Randomly generated strings with letters, Western and Arabic-Indic digits, punctuation, and symbols (e.g., !?.:;,@#$%). Formats include plain text, phone numbers, dates, and mixed sequences.

Font Sizes: Randomized between 44–84 pixels (mean ~64px) for varied text scales. Fonts: Arabic: Almarai, Amiri, Cairo, IBM Plex Sans Arabic, Markazi Text, Scheherazade New, Tajawal, Lateef. English: Arimo Nerd Font, Fira Mono, Montserrat, Oswald, Poppins, Source Code Pro, Lato, Open Sans, Playfair Display, Raleway.

Colors: Text: Dark RGB (0–88, 0–88, 0–88) for readability. Background: Light RGB (150–255, 150–255, 150–255) with Gaussian noise (0–50, 0–50, 0–50) for realism.

Image Format: JPEG, with configurable width/height based on text and font size. Augmentations: No geometric augmentations applied; rely on training-time augmentations (e.g., rotation, flipping) for additional variation.

Usage This dataset is ideal for:

Training and fine-tuning OCR models for Arabic and English text. Evaluating performance on mixed-language, RTL scripts, and diverse character sets. Research in multilingual OCR, especially for challenging scripts like Arabic.

Example Code import os

dataset_dir = 'Arabic_English_OCR_Dataset' # Update with your dataset path ar_dir = os.path.join(dataset_dir, 'ar') en_dir = os.path.join(dataset_dir, 'en')

Load labels

ar_labels = [] with open(os.path.join(ar_dir, 'labels.txt'), 'r', encoding='utf8') as f: for line in f: image_name, text = line.strip().split(' ', 1) ar_labels.append((image_name, text))

en_labels = [] with open(os.path.join(en_dir, 'labels.txt'), 'r', encoding='utf8') as f: for line in f: image_name, text = line.strip().split(' ', 1) en_labels.append((image_name, text))

Load images

ar_images = [os.path.join(ar_dir, name) for name, _ in ar_labels] en_images = [os.path.join(en_dir, name) for name, _ in en_labels]

Notes

Synthetic Nature: As a synthetic dataset, it may not capture all real-world complexities (e.g., lighting, occlusions). Complement with real-world data or training-time augmentations (e.g., rotation, brightness) for optimal performance. Character Set: Includes 151 unique characters (Arabic, English, digits, symbols). See labels.txt for details. Image Dimensions: Vary based on text length, with mean height ~64px and width ~510px (Arabic) or ~777px (English).

License Released under the MIT License. The source text from the Arabic News Summarization dataset follows its respective license (see Hugging Face for details). Acknowledgments

TextRecognitionDataGenerator: For the image generation tool. Arabic News Summarization: For source text data (Hugging Face). Google Fonts: For open-source fonts used in image generation.

This dataset contains all recorded and hand-annotated as well as all synthetically generated data as well as representative trained networks used for detection and tracking experiments in the replicAnt - generating annotated images of animals in complex environments using Unreal Engine manuscript. Unless stated otherwise, all 3D animal models used in the synthetically generated data have been generated with the open-source photgrammetry platform scAnt peerj.com/articles/11155/. All synthetic data has been generated with the associated replicAnt project available from https://github.com/evo-biomech/replicAnt.

Abstract:

Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.

Benchmark data

Two video datasets were curated to quantify detection performance; one in laboratory and one in field conditions. The laboratory dataset consists of top-down recordings of foraging trails of Atta vollenweideri (Forel 1893) leaf-cutter ants. The colony was collected in Uruguay in 2014, and housed in a climate chamber at 25°C and 60% humidity. A recording box was built from clear acrylic, and placed between the colony nest and a box external to the climate chamber, which functioned as feeding site. Bramble leaves were placed in the feeding area prior to each recording session, and ants had access to the recording area at will. The recorded area was 104 mm wide and 200 mm long. An OAK-D camera (OpenCV AI Kit: OAK-D, Luxonis Holding Corporation) was positioned centrally 195 mm above the ground. While keeping the camera position constant, lighting, exposure, and background conditions were varied to create recordings with variable appearance: The “base” case is an evenly lit and well exposed scene with scattered leaf fragments on an otherwise plain white backdrop. A “bright” and “dark” case are characterised by systematic over- or underexposure, respectively, which introduces motion blur, colour-clipped appendages, and extensive flickering and compression artefacts. In a separate well exposed recording, the clear acrylic backdrop was substituted with a printout of a highly textured forest ground to create a “noisy” case. Last, we decreased the camera distance to 100 mm at constant focal distance, effectively doubling the magnification, and yielding a “close” case, distinguished by out-of-focus workers. All recordings were captured at 25 frames per second (fps).

The field datasets consists of video recordings of Gnathamitermes sp. desert termites, filmed close to the nest entrance in the desert of Maricopa County, Arizona, using a Nikon D850 and a Nikkor 18-105 mm lens on a tripod at camera distances between 20 cm to 40 cm. All video recordings were well exposed, and captured at 23.976 fps.

Each video was trimmed to the first 1000 frames, and contains between 36 and 103 individuals. In total, 5000 and 1000 frames were hand-annotated for the laboratory- and field-dataset, respectively: each visible individual was assigned a constant size bounding box, with a centre coinciding approximately with the geometric centre of the thorax in top-down view. The size of the bounding boxes was chosen such that they were large enough to completely enclose the largest individuals, and was automatically adjusted near the image borders. A custom-written Blender Add-on aided hand-annotation: the Add-on is a semi-automated multi animal tracker, which leverages blender’s internal contrast-based motion tracker, but also include track refinement options, and CSV export functionality. Comprehensive documentation of this tool and Jupyter notebooks for track visualisation and benchmarking is provided on the replicAnt and BlenderMotionExport GitHub repositories.

Synthetic data generation

Two synthetic datasets, each with a population size of 100, were generated from 3D models of \textit{Atta vollenweideri} leaf-cutter ants. All 3D models were created with the scAnt photogrammetry workflow. A “group” population was based on three distinct 3D models of an ant minor (1.1 mg), a media (9.8 mg), and a major (50.1 mg) (see 10.5281/zenodo.7849059)). To approximately simulate the size distribution of A. vollenweideri colonies, these models make up 20%, 60%, and 20% of the simulated population, respectively. A 33% within-class scale variation, with default hue, contrast, and brightness subject material variation, was used. A “single” population was generated using the major model only, with 90% scale variation, but equal material variation settings.

A Gnathamitermes sp. synthetic dataset was generated from two hand-sculpted models; a worker and a soldier made up 80% and 20% of the simulated population of 100 individuals, respectively with default hue, contrast, and brightness subject material variation. Both 3D models were created in Blender v3.1, using reference photographs.

Each of the three synthetic datasets contains 10,000 images, rendered at a resolution of 1024 by 1024 px, using the default generator settings as documented in the Generator_example level file (see documentation on GitHub). To assess how the training dataset size affects performance, we trained networks on 100 (“small”), 1,000 (“medium”), and 10,000 (“large”) subsets of the “group” dataset. Generating 10,000 samples at the specified resolution took approximately 10 hours per dataset on a consumer-grade laptop (6 Core 4 GHz CPU, 16 GB RAM, RTX 2070 Super).

Additionally, five datasets which contain both real and synthetic images were curated. These “mixed” datasets combine image samples from the synthetic “group” dataset with image samples from the real “base” case. The ratio between real and synthetic images across the five datasets varied between 10/1 to 1/100.

Funding

This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

NOTE: Transactions which are detected as fraud are cancelled, so for fraud detection these columns (oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest ) must not be used.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).

type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

amount - amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction.

nameDest - customer who is the recipient of the transaction

oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).

newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.

isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction.

Past Research

There are 5 similar files that contain the run of 5 different scenarios. These files are better explained at my PhD thesis chapter 7 (PhD Thesis Available here http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932.

We ran PaySim several times using random seeds for 744 steps, representing each hour of one month of real time, which matches the original logs. Each run took around 45 minutes on an i7 intel processor with 16GB of RAM. The final result of a run contains approximately 24 million of financial records divided into the 5 types of categories: CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

Acknowledgements

This work is part of the research project ”Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

The data annotation and labeling tools market is experiencing robust growth, driven by the escalating demand for high-quality training data in the burgeoning fields of artificial intelligence (AI) and machine learning (ML). The market's expansion is fueled by the increasing adoption of AI across diverse sectors, including autonomous vehicles, healthcare, and finance. These industries require vast amounts of accurately labeled data to train their AI models, leading to a significant surge in the demand for efficient and scalable annotation tools. While precise market sizing for 2025 is unavailable, considering a conservative estimate and assuming a CAGR of 25% (a reasonable figure given industry growth), we can project a market value exceeding $2 billion in 2025, rising significantly over the forecast period (2025-2033). Key trends include the growing adoption of cloud-based solutions, increased automation in the annotation process through AI-assisted tools, and a heightened focus on data privacy and security. The rise of synthetic data generation is also beginning to impact the market, offering potential cost savings and improved data diversity. However, challenges remain. The high cost of skilled annotators, the need for continuous quality control, and the inherent complexities of labeling diverse data types (images, text, audio, video) pose significant restraints on market growth. While leading players like Labelbox, Scale AI, and SuperAnnotate dominate the market with advanced features and robust scalability, smaller companies and open-source tools continue to compete, often focusing on niche applications or offering cost-effective alternatives. The competitive landscape is dynamic, with continuous innovation and mergers and acquisitions shaping the future of this rapidly evolving market. Regional variations in adoption are also expected, with North America and Europe likely leading the market, followed by Asia-Pacific and other regions. This continuous evolution necessitates careful strategic planning and adaptation for businesses operating in or considering entry into this space.

3DHBD 3D Humanix Blender Dataset for Student pose detection applications.

1.PUBLICATION<

2024 3DHBD: Synthetic 3D Dataset for Advanced Student Behavior Analysis in Educational Environments

Journal: Balochistan Journal of Engineering & Applied Sciences (BJEAS)

Status: Published [Paper Link]

2.PUBLICATION<

2024

Advanced Student Behavior Analysis Using Dual-Model Approach for Pose and Emotion Detection

Journal: Multimedia Tools and Applications by Springer

Status: Under review

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12492341%2F4b1386e616cde951af00ae0fa5311b61%2Fc1_normal1.png?generation=1715518170668594&alt=media" alt="">

Overview: 3DHBD (3D Humanix Blender Dataset) is a high-quality synthetic dataset developed using Blender, an open-source and freely accessible software. Due to privacy and security concerns surrounding student data, suitable datasets for student pose detection are scarce. 3DHBD addresses this gap by providing a comprehensive dataset aimed at detection of abnormal behaviour of students in crowded educational environments.

Author Introduction: This dataset was created to fulfill the thesis requirements for a master's degree. This project was created by Hamza Iqbal, [ Linkedin, Github ] who completed his Master's Degree in Electrical Engineering (Signal & Image Processing) from the prestigious Institute of Space Technology (IST), Islamabad, Pakistan in July 2024. Hamza previously holds Bachelor's Degree in Electrical Engineering (Electronics) from Bahria University, Islamabad.

He worked under the supervision of Dr. Madiha Tahir, an Assistant Professor at IST. Dr. Madiha’s research interests lie in the image processing and machine learning domains. [Google Scholar ID]

Key Features of the Dataset: 1. Synthetic Generation: All data is synthetic, ensuring that no actual student information is used. This maintains the dataset's privacy and security integrity. 2. Blender-Based: Created with Blender, an open-source software, it guarantees flexibility for researchers and is freely accessible. 3. High-Quality Labels: Precise labeling of student poses ensures reliable and consistent data for training and testing. 4. Diverse Poses: The dataset contains a diverse range of student poses, enabling more robust model training for pose detection. 5. Educational Context: The dataset is specifically curated for educational settings, making it highly relevant for researchers focused on classroom behavior analysis. 6. Robust Supervision: The dataset was developed under the guidance of an experienced faculty member, ensuring high academic standards and data quality.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12492341%2F4944c3b1864cf469e8b156bce39029ff%2Fist.png?generation=1720936700302708&alt=media" alt="">

The NIST BGP RPKI IO framework (BRIO) is a test tool only subset of the BGP-SRx Framework. It is an open source implementation and test platform that allows the synthetic generation of test data for emerging BGP security extensions such as RPKI Origin Validation and BGPSec Path Validation and ASPA validation. BRIO is designed in such that it allows the creation of stand alone testbeds, loaded with freely configurable scenarios to study secure BGP implementations. As a result, much functionality is provided.

AI in Generative Adversarial Networks (GANs) Market Outlook

According to our latest research, the global AI in Generative Adversarial Networks (GANs) market size reached USD 2.65 billion in 2024, reflecting robust growth driven by rapid advancements in deep learning and artificial intelligence. The market is expected to register a remarkable CAGR of 31.4% from 2025 to 2033, accelerating the adoption of GANs across diverse industries. By 2033, the market is forecasted to achieve a value of USD 32.78 billion, underscoring the transformative impact of GANs in areas such as image and video generation, data augmentation, and synthetic content creation. This trajectory is supported by the increasing demand for highly realistic synthetic data and the expansion of AI-driven applications across enterprise and consumer domains.

A primary growth factor for the AI in Generative Adversarial Networks market is the exponential increase in the availability and complexity of data that organizations must process. GANs, with their unique adversarial training methodology, have proven exceptionally effective for generating realistic synthetic data, which is crucial for industries like healthcare, automotive, and finance where data privacy and scarcity are significant concerns. The ability of GANs to create high-fidelity images, videos, and even text has enabled organizations to enhance their AI models, improve data diversity, and reduce bias, thereby accelerating the adoption of AI-driven solutions. Furthermore, the integration of GANs with cloud-based platforms and the proliferation of open-source GAN frameworks have democratized access to this technology, enabling both large enterprises and SMEs to harness its potential for innovative applications.

Another significant driver for the AI in Generative Adversarial Networks market is the surge in demand for advanced content creation tools in media, entertainment, and marketing. GANs have revolutionized the way digital content is produced by enabling hyper-realistic image and video synthesis, deepfake generation, and automated design. This has not only streamlined creative workflows but also opened new avenues for personalized content, virtual influencers, and immersive experiences in gaming and advertising. The rapid evolution of GAN architectures, such as StyleGAN and CycleGAN, has further enhanced the quality and scalability of generative models, making them indispensable for enterprises seeking to differentiate their digital offerings and engage customers more effectively in a highly competitive landscape.

The ongoing advancements in hardware acceleration and AI infrastructure have also played a pivotal role in propelling the AI in Generative Adversarial Networks market forward. The availability of powerful GPUs, TPUs, and AI-specific chips has significantly reduced the training time and computational costs associated with GANs, making them more accessible for real-time and large-scale applications. Additionally, the growing ecosystem of AI services and consulting has enabled organizations to overcome technical barriers, optimize GAN deployments, and ensure compliance with evolving regulatory standards. As investment in AI research continues to surge, the GANs market is poised for sustained innovation and broader adoption across sectors such as healthcare diagnostics, autonomous vehicles, financial modeling, and beyond.

From a regional perspective, North America continues to dominate the AI in Generative Adversarial Networks market, accounting for the largest share in 2024, driven by its robust R&D ecosystem, strong presence of leading technology companies, and early adoption of AI technologies. Europe follows closely, with significant investments in AI research and regulatory initiatives promoting ethical AI development. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, expanding AI talent pool, and increasing government support for AI innovation. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as enterprises in these regions begin to explore the potential of GANs for industry-specific applications.

Component Analysis

The AI in Generative Adversarial Networks market is segmented by component into software, hardware, and services, each playing a vital role in the ecosystem’s development and adoption. Software solutions constitute the largest share of the market in 2024, reflecting the growing demand for ad

Evaluation Dataset Curation for LLMs Market Outlook

According to our latest research, the global Evaluation Dataset Curation for LLMs market size reached USD 1.18 billion in 2024, reflecting robust momentum driven by the proliferation of large language models (LLMs) across industries. The market is projected to expand at a CAGR of 24.7% from 2025 to 2033, reaching a forecasted value of USD 9.01 billion by 2033. This impressive growth is primarily fueled by the surging demand for high-quality, unbiased, and diverse datasets essential for evaluating, benchmarking, and fine-tuning LLMs, as well as for ensuring their safety and fairness in real-world applications.

The exponential growth of the Evaluation Dataset Curation for LLMs market is underpinned by the rapid advancements in artificial intelligence and natural language processing technologies. As organizations increasingly deploy LLMs for a variety of applications, the need for meticulously curated datasets has become paramount. High-quality datasets are the cornerstone for testing model robustness, identifying biases, and ensuring compliance with ethical standards. The proliferation of domain-specific use cases—from healthcare diagnostics to legal document analysis—has further intensified the demand for specialized datasets tailored to unique linguistic and contextual requirements. Moreover, the growing recognition of dataset quality as a critical determinant of model performance is prompting enterprises and research institutions to invest heavily in advanced curation platforms and services.

Another significant growth driver for the Evaluation Dataset Curation for LLMs market is the heightened regulatory scrutiny and societal emphasis on AI transparency, fairness, and accountability. Governments and standard-setting bodies worldwide are introducing stringent guidelines to mitigate the risks associated with biased or unsafe AI systems. This regulatory landscape is compelling organizations to adopt rigorous dataset curation practices, encompassing bias detection, fairness assessment, and safety evaluations. As LLMs become integral to decision-making processes in sensitive domains such as finance, healthcare, and public policy, the imperative for trustworthy and explainable AI models is fueling the adoption of comprehensive evaluation datasets. This trend is expected to accelerate as new regulations come into force, further expanding the market’s scope.

The market is also benefiting from the collaborative efforts between academia, industry, and open-source communities to establish standardized benchmarks and best practices for LLM evaluation. These collaborations are fostering innovation in dataset curation methodologies, including the use of synthetic data generation, crowdsourcing, and automated annotation tools. The integration of multimodal data—combining text, images, and code—is enabling more holistic assessments of LLM capabilities, thereby expanding the market’s addressable segments. Additionally, the emergence of specialized startups focused on dataset curation services is introducing competitive dynamics and driving technological advancements. These factors collectively contribute to the market’s sustained growth trajectory.

Regionally, North America continues to dominate the Evaluation Dataset Curation for LLMs market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is home to leading AI research institutions, technology giants, and a vibrant ecosystem of startups dedicated to LLM development and evaluation. Europe is witnessing increased investments in AI ethics and regulatory compliance, while Asia Pacific is rapidly emerging as a key growth market due to its expanding AI research capabilities and government-led digital transformation initiatives. Latin America and the Middle East & Africa are also showing promise, albeit from a smaller base, as local enterprises and public sector organizations begin to recognize the strategic importance of robust LLM evaluation frameworks.

Da

Snapshot img { margin: 10px !important; } AI Text-to-image Generator Market Size 2025-2029

The ai text-to-image generator market size is forecast to increase by USD 1.6 billion, at a CAGR of 34.5% between 2024 and 2029.

The global AI text-to-image generator market is advancing, driven primarily by technological leaps in generative model quality, enabling the creation of highly realistic and coherent visual content. This improvement in ai creativity and art generation has expanded the technology's utility from a novelty to a practical tool for professionals. A defining trend is the pivot toward enterprise-grade solutions built on commercial safety and legal indemnification. This shift is a response to the profound legal and reputational risks associated with models trained on undifferentiated internet data. As part of this, the development of a robust multimodal ai model is becoming a key area of focus for integrated content strategies.The market's evolution is shaped by the need for commercially viable platforms that offer proprietary models trained on meticulously curated and fully licensed datasets. While these platforms provide the assurance of legal compliance, the industry's foundation on datasets scraped from the public internet has created a complex ethical and regulatory landscape. Unresolved issues surrounding copyright infringement for this ai image generator and the lack of a clear legal framework create significant uncertainty. This environment makes it difficult for businesses to develop long-term strategies, as the rules for ai-based image analysis and ownership of AI-generated content remain undefined, representing a significant barrier to mainstream trust.

What will be the Size of the AI Text-to-image Generator Market during the forecast period?

Explore in-depth regional segment analysis with market size data with forecasts 2025-2029 - in the full report.
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The global AI text-to-image generator market is fundamentally shaped by the evolving model architecture, with diffusion models advancing beyond generative adversarial networks. The ability of these systems to achieve superior semantic interpretation of natural language prompts is a critical dynamic, improving prompt understanding for greater image fidelity and compositional coherence. Challenges persist in areas like accurate text rendering in images and maintaining character consistency and style consistency across generations. Nevertheless, the expanding stylistic versatility, from photorealistic synthesis to abstract art, alongside generative fill techniques, positions these tools as central to AI-assisted creation within broader multimodal AI systems.Market development is increasingly tied to enterprise-grade platforms offering API integration, commercial use license options, and legal indemnification. Operational concerns such as computational cost, inference cost, and energy consumption are being addressed through model fine-tuning. Responsible deployment necessitates algorithmic bias mitigation via careful training data curation and the use of licensed datasets for synthetic data generation. Advanced user controls through prompt engineering and latent space manipulation are becoming common, alongside in-painting capabilities and out-painting functionality. For content provenance, digital watermarking is a key area of development. The market is projected to expand by over 25% as capabilities extend into text-to-video generation, image-to-video synthesis, and text-to-3D synthesis.

How is this AI Text-to-image Generator Market segmented?

The ai text-to-image generator market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029,for the following segments. ComponentSoftwareServicesDeploymentCloud-basedOn-premisesEnd-userIndividualEnterpriseGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceSpainItalyThe NetherlandsAPACChinaSouth KoreaJapanIndiaAustraliaIndonesiaSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaSouth AfricaUAETurkeyRest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The software segment is the core of the market, encompassing platforms, applications, and APIs that synthesize images from text. This area is characterized by rapid product evolution, with offerings including standalone consumer platforms and, increasingly, software integrated into larger creative and productivity ecosystems. This integration is of strategic importance as it embeds generative capabilities within existing professional workflows. In a key region, over 80% of market value is concentrated in a single country, underscoring the importance of established software ecosystems for driving adoption.A critical trend shaping this segment is the bifurcation between open-source models and proprietary

Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-world images remains a problem. Therefore, based on a real-world application, we explored the generation of images with physics-based rendering for an industrial object detection task. Setting up the render engine’s environment requires a lot of choices and parameters. One fundamental question is whether to apply the concept of domain randomization or use domain knowledge to try and achieve photorealism. To answer this question, we compared different strategies for setting up lighting, background, object texture, additional foreground objects and bounding box computation in a data-centric approach. We compared the resulting average precision from generated images with different levels of realism and variability. In conclusion, we found that domain randomization is a viable strategy for the detection of industrial objects. However, domain knowledge can be used for object-related aspects to improve detection performance. Based on our results, we provide guidelines and an open-source tool for the generation of synthetic images for new industrial applications.

Augmented Texas 7000-bus synthetic grid Augmented version of the synthetic Texas 7k dataset published by Texas A&M University. The system has been populated with high-resolution distributed photovoltaic (PV) generation, comprising 4,499 PV plants of varying sizes with associated time series for 1 year of operation. This high-resolution dataset was produced following publicly available data and it is free of CEII. Details on the procedure followed to generate the PV dataset can be found in the Open COG Grid Project Year 1 Report (Chapter 6). The technical data of the system is provided using the (open) CTM specification for easy accessibility from Python without additional packages (data can be loaded as a dictionary). The time series for demand and PV production are provided as a HDF5 file, also loadable with standard open-source tools. We additionally provide example scripts for parsing the data in Python. Prepared by LLNL under Contract DE-AC52-07NA27344. LLNL control number: LLNL-DATA-2001833.

Generative AI Market Outlook

According to our latest research, the global Generative AI (Gen AI) market size reached USD 15.8 billion in 2024 and is projected to grow at a compound annual growth rate (CAGR) of 36.2% from 2025 to 2033. By the end of the forecast period, the market is expected to attain a value of USD 221.5 billion. This remarkable growth is fueled by rapid advancements in deep learning algorithms, the proliferation of large language models, and an increasing demand for automation and content generation across various industries. As per our latest research, the adoption of generative AI is transforming business processes, driving innovation, and unlocking new revenue streams on a global scale.

One of the primary growth factors for the generative AI market is the exponential increase in data availability and computational power. With the emergence of high-performance GPUs and cloud-based infrastructure, enterprises can now process vast datasets and train sophisticated generative models more efficiently. This technological leap has enabled organizations to deploy AI solutions that can generate human-like text, images, audio, and even code, thereby enhancing productivity and creativity. The integration of generative AI into business workflows is also reducing operational costs and time-to-market for new products and services, making it an indispensable tool for digital transformation initiatives.

Another significant driver is the widespread adoption of generative AI in key verticals such as healthcare, finance, media and entertainment, and retail. In healthcare, generative AI is revolutionizing drug discovery, medical imaging analysis, and personalized patient care by generating synthetic data and predictive models. The financial sector leverages generative AI for fraud detection, risk assessment, and the automation of customer service interactions. Meanwhile, media and entertainment companies are utilizing these technologies to create hyper-personalized content, automate video editing, and enhance visual effects. Retailers are using generative AI to optimize inventory management, personalize marketing campaigns, and improve customer engagement through AI-generated product descriptions and recommendations.

The increasing focus on ethical AI, regulatory compliance, and responsible AI development is also shaping the growth trajectory of the generative AI market. Governments and industry bodies are introducing guidelines and standards to ensure transparency, accountability, and fairness in AI-generated content. This has led to the emergence of AI governance frameworks and tools that help organizations monitor and mitigate potential biases or misuse of generative models. As companies invest in responsible AI practices, they are gaining competitive advantages by building trust with customers and regulators, further accelerating the adoption of generative AI solutions.

From a regional perspective, North America continues to dominate the generative AI market, accounting for the largest share in 2024 due to its robust technological infrastructure, strong presence of leading AI vendors, and high investment in research and development. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, government initiatives to promote AI innovation, and a burgeoning startup ecosystem. Europe is also witnessing significant growth, fueled by increasing adoption in manufacturing and automotive sectors, as well as a strong focus on data privacy and AI ethics. The Middle East & Africa and Latin America are gradually catching up, supported by growing awareness and investments in AI-driven transformation.

Component Analysis

The generative AI market is segmented by component into software, hardware, and services, each playing a pivotal role in the ecosystem. The software segment currently holds the largest share, primarily due to the proliferation of generative models such as GPT, DALL-E, and Stable Diffusion. These software solutions offer advanced capabilities in natural language processing, image synthesis, and content generation, making them highly sought after by enterprises across industries. The availability of open-source frameworks and pre-trained models has further lowered the barriers to entry, enabling businesses of all sizes to experiment with and deploy generative AI applications at scale.

Hardware is another critical segment, as the training and inference of

This comprehensive dataset contains 25,000 synthetic text samples designed for machine learning research in cryptocurrency address detection. The dataset simulates realistic dark web communication patterns while maintaining complete ethical compliance through synthetic data generation. 🔑 Key Features:

25,000 text samples with balanced distribution 8 cryptocurrency types supported (Bitcoin, Ethereum, Litecoin, etc.) 25+ engineered features for advanced ML analysis Ethical synthetic data - no real dark web content used Research-ready format with comprehensive labels High-quality annotations for supervised learning

📈 Dataset Statistics MetricValueDescriptionTotal Samples25,000Complete dataset sizePositive Samples10,000 (40%)Contains cryptocurrency addressesNegative Samples12,500 (50%)No cryptocurrency contentAmbiguous Cases2,500 (10%)Edge cases and borderline examplesFeatures25+Comprehensive feature engineeringCryptocurrencies8 typesBitcoin, Ethereum, Litecoin, Monero, etc.LanguagesEnglishPrimary language for text content

🏗️ Dataset Structure Core Columns:

text: Raw text content (sample communications) contains_crypto: Binary label (0/1) indicating presence of crypto addresses crypto_type: Type of cryptocurrency detected (bitcoin_legacy, ethereum, etc.) address_found: Actual cryptocurrency address if present confidence_label: Quality indicator (high/medium/low)

Feature Engineering Columns (25+ features):

text_length, word_count, sentence_count: Basic text statistics char_diversity, digit_ratio, uppercase_ratio: Character analysis bitcoin_legacy_count, ethereum_count, litecoin_count: Crypto pattern counts crypto_keyword_count, crypto_keyword_density: Contextual features hex_long_sequences, alphanumeric_long: Pattern analysis has_urgent_words, has_security_words: Semantic indicators And many more sophisticated features...

Metadata Columns:

dataset_version: Version tracking (1.0) generation_method: Data creation method (synthetic) research_purpose: Intended use (crypto_address_detection) ethical_compliance: Compliance status (synthetic_data_only)

🎯 Use Cases & Applications Academic Research:

Cybersecurity Studies: Cryptocurrency forensics research Machine Learning: Text classification and pattern recognition Digital Forensics: Automated evidence detection systems Natural Language Processing: Financial text analysis

Industry Applications:

Law Enforcement: Automated dark web monitoring systems Financial Security: Anti-money laundering tools Compliance: Regulatory technology solutions Cybersecurity: Threat intelligence platforms

Educational Purposes:

University Courses: Cybersecurity and ML education Research Training: Graduate student projects Kaggle Competitions: Machine learning challenges Open Source Projects: Community-driven research

🧪 Synthetic Data Methodology Why Synthetic Data?

Ethical Compliance: No real personal data or illegal content Legal Safety: Avoids copyright and privacy violations Research Reproducibility: Other researchers can replicate exactly Quality Control: Perfect labeling and controlled variations Scalability: Can generate unlimited samples as needed

Data Generation Process:

Pattern Analysis: Based on academic research of real dark web patterns Template Creation: Realistic communication templates developed Address Generation: Mathematically valid cryptocurrency addresses Noise Addition: Realistic variations and edge cases included Feature Engineering: Comprehensive feature extraction applied

Validation Against Real Data:

Patterns validated against published academic research Address formats match real cryptocurrency specifications Communication styles based on documented dark web studies 95%+ similarity to real dark web communication patterns

📊 Benchmark Results Machine Learning Performance:

Random Forest: 87-92% accuracy Logistic Regression: 85-89% accuracy Gradient Boosting: 88-91% accuracy SVM: 86-90% accuracy Ensemble Method: 90-95% accuracy

Cross-Validation Results:

5-Fold CV Mean: 0.88-0.92 F1-Score Standard Deviation: <0.03 (consistent performance) Overfitting Check: CV-Test gap <0.05 (excellent generalization)

🔬 Technical Specifications File Format:

Primary File: crypto_detection_research_dataset.csv Encoding: UTF-8 Size: ~15-25 MB (depending on feature inclusion) Format: CSV with headers

Data Types:

Text Fields: String (UTF-8 encoded) Labels: Integer (0/1 binary) Features: Float64 (normalized numerical features) Categories: String (categorical labels)

Quality Assurance:

No Missing Values: Complete dataset with all fields populated Balanced Distribution: Careful attention to class balance Feature Scaling: Normalized features ready for ML algorithms Validation: Extensive testing and verification performed

Getting Started

Quick Start Code: pythonimport pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassif...

Privacy-Preserving Synthetic Voice Market Outlook

According to our latest research, the global Privacy-Preserving Synthetic Voice market size reached USD 1.28 billion in 2024, supported by a robust surge in privacy-centric AI applications across industries. The market is expected to grow at a remarkable CAGR of 26.1% from 2025 to 2033, projecting a value of USD 10.82 billion by 2033. This exponential growth is primarily driven by the rising demand for secure, AI-enabled voice solutions that safeguard sensitive user data while enabling seamless human-computer interaction. As privacy regulations tighten and digital transformation accelerates, organizations are increasingly prioritizing privacy-preserving technologies in their voice-based solutions to foster trust and compliance.

One of the key growth factors for the Privacy-Preserving Synthetic Voice market is the global escalation of privacy concerns and regulatory frameworks such as GDPR, CCPA, and emerging data protection laws in Asia Pacific. These regulations mandate stringent data handling and consent protocols, compelling enterprises to adopt synthetic voice technologies that integrate privacy-preserving mechanisms by design. The demand is further amplified by high-profile data breaches and growing consumer awareness regarding the misuse of biometric and voice data. As organizations strive to maintain customer trust and avoid legal repercussions, privacy-preserving synthetic voice solutions offer a strategic advantage by anonymizing, encrypting, and securely processing voice data, thus enabling compliance and competitive differentiation.

Another significant driver is the proliferation of voice-enabled applications across diverse sectors such as healthcare, finance, and customer service. In these sensitive domains, the ability to generate lifelike synthetic voices without compromising user privacy is paramount. Healthcare providers, for instance, are leveraging privacy-preserving voice synthesis for telehealth, patient engagement, and accessibility services, ensuring that patient information remains confidential. Similarly, financial institutions are deploying these solutions in customer support and authentication processes to prevent identity theft and fraud. The integration of advanced AI models, federated learning, and edge computing further enhances the privacy and performance of synthetic voice systems, fueling their adoption across both B2B and B2C markets.

Technological advancements and the democratization of AI development tools are also accelerating market growth. The emergence of open-source frameworks, cloud-based AI services, and hardware accelerators has lowered the barriers to entry, enabling startups and established players alike to innovate rapidly. This has led to the creation of highly customizable and scalable privacy-preserving synthetic voice solutions tailored to specific industry needs. The convergence of natural language processing, deep learning, and secure multi-party computation is enabling more accurate, expressive, and context-aware synthetic voices while maintaining robust privacy safeguards. As a result, enterprises are able to deploy voice interfaces in high-stakes environments such as legal, government, and education, further expanding the addressable market.

The integration of Edge-AI Privacy-Preserving Virtual Scribe technology is becoming increasingly significant in the healthcare sector. This innovative approach allows for real-time transcription and documentation of patient interactions while ensuring that sensitive data remains secure. By processing data locally on edge devices, healthcare providers can maintain patient confidentiality and comply with stringent privacy regulations. This technology not only enhances operational efficiency but also improves the accuracy of medical records, leading to better patient outcomes. As the demand for digital health solutions grows, Edge-AI Privacy-Preserving Virtual Scribe is set to play a crucial role in transforming healthcare delivery.

From a regional perspective, North America currently dominates the Privacy-Preserving Synthetic Voice market, accounting for over 38% of the global revenue in 2024, followed by Europe and Asia Pacific. The strong presence of leading AI technology providers, early adoption of privacy regulations, and a mature digital

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.

Context

Data collection is perhaps the most crucial part of any machine learning model: without it being done properly, not enough information is present for the model to learn from the patterns leading to one output or another. Data collection is however a very complex endeavor, time-consuming due to the volume of data that needs to be acquired and annotated. Annotation is an especially problematic step, due to its difficulty, length, and vulnerability to human error and inaccuracies when annotating complex data.

With high processing power becoming ever more accessible, synthetic dataset generation is becoming a viable option when looking to generate large volumes of accurately annotated data. With the help of photorealistic renderers, it is for example possible now to generate immense amounts of data, annotated with pixel-perfect precision and whose content is virtually indistinguishable from real-world pictures.

As an exercise of synthetic dataset generation, the data offered here was generated using the Python API of Blender, with the images rendered through the Cycles raycaster. It represents plausible images representing pictures of chessboard and pieces. The goal is, from those pictures and their annotation, to build a model capable of recognizing the pieces, as well as their positions on the board.

Content

The dataset contains a large amount of synthetic, randomly generated images representing pictures of chess images, taken at an angle overlooking the board and its pieces. Each image is associated with a .json file containing its annotations. The naming convention is that each render is associated with a number X, and that the images and annotations associated with that render are respectively named X.jpg and X.json.

The data has been generated using the Python scripts and .blend file present in this repository. The chess board and pieces models that have been used for those renders are not provided with the code.

Data characteristics :

• Images : 1280x1280 JPEG images representing pictures of chess game boards.
• Annotations : JSON files containing two variables :
  • "config", a dictionary associating a cell to the type of piece it contains. If a cell is not presented in the keys, it means that it is empty.
  • "corners", a 4x2 list which contains the coordinates, in the image, of the board corners. Those corners coordinates are normalized to the [0;1] range.
• config.json : A JSON file generated before rendering, which contains variables relative to the constant properties of the boards in the renders :
  • "cellsCoordinates", a dictionary associating a cell name to its coordinates on the board. We have for example
  • "piecesTypes", a list of strings containing the types of pieces present in the renders.

No distinction has been hard-built between training, validation, and testing data, and is left completely up to the users. A proposed pipeline for the extraction, recognition, and placement of chess pieces is proposed in a notebook added with this dataset.

Acknowledgements

I would like to express my gratitude for the efforts of the Blender Foundation and all its participants, for their incredible open-source tool which once again has allowed me to conduct interesting projects with great ease.

Inspiration

Two interesting papers on the generation and use of synthetic data, which have inspired me to conduct this project :

Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt (2021) Fake It Till You Make It: Face analysis in the wild using synthetic data alone https://arxiv.org/abs/2109.15102 Salehe Erfanian Ebadi, You-Cyuan Jhang, Alex Zook (2021) PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision https://arxiv.org/abs/2112.09290