100% synthetic. Based on model-released photos. Can be used for any purpose except for the ones violating the law. Worldwide. Different backgrounds: colored, transparent, photographic. Diversity: ethnicity, demographics, facial expressions, and poses.

This file is supplementary material for the manuscript Racial Bias in AI-Generated Images, which has been submitted to a peer-reviewed journal. This dataset/paper examined the image-to-image generation accuracy (i.e., the original race and gender of a person’s image were replicated in the new AI-generated image) of a Chinese AI-powered image generator. We examined the image-to-image generation models transforming the racial and gender categories of the original photos of White, Black and East Asian people (N =1260) in three different racial photo contexts: a single person, two people of the same race, and two people of different races.

The quality of AI-generated images has rapidly increased, leading to concerns of authenticity and trustworthiness.

CIFAKE is a dataset that contains 60,000 synthetically-generated images and 60,000 real images (collected from CIFAR-10). Can computer vision techniques be used to detect when an image is real or has been generated by AI?

Dataset details The dataset contains two classes - REAL and FAKE. For REAL, we collected the images from Krizhevsky & Hinton's CIFAR-10 dataset For the FAKE images, we generated the equivalent of CIFAR-10 with Stable Diffusion version 1.4 There are 100,000 images for training (50k per class) and 20,000 for testing (10k per class)

References If you use this dataset, you must cite the following sources

Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images.

Bird, J.J., Lotfi, A. (2023). CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images. arXiv preprint arXiv:2303.14126.

Real images are from Krizhevsky & Hinton (2009), fake images are from Bird & Lotfi (2023). The Bird & Lotfi study is a preprint currently available on ArXiv and this description will be updated when the paper is published.

License This dataset is published under the same MIT license as CIFAR-10:

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Building upon Google's research Rich Human Feedback for Text-to-Image Generation we have collected over 1.5 million responses from 152'684 individual humans using Rapidata via the Python API. Collection took roughly 5 days. If you get value from this dataset and would like to see more in the future, please consider liking it.

  Overview

We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback.

Description:

👉 Download the dataset here

This dataset consists of 20,000 image-text pairs designed to aid in training machine learning models capable of extracting text from scanned Telugu documents. The images in this collection resemble “scans” of documents or book pages, paired with their corresponding text sequences. This dataset aims to reduce the necessity for complex pre-processing steps, such as bounding-box creation and manual text labeling, allowing models to directly map from image inputs to textual sequences.

The main objective is to train models to handle real-world scans, particularly those from aged or damaged documents, without needing to design elaborate computer vision algorithms. The dataset focuses on minimizing the manual overhead involved in traditional document processing methods, making it a valuable resource for tasks like optical character recognition (OCR) in low-resource languages like Telugu.

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Key Features:

Wide Variety of Realistic Scans: The dataset includes images mimicking realistic variations, such as aging effects, smudges, and incomplete characters, commonly found in physical book scans or older documents.

Image-Text Pairing: Each image is linked with its corresponding pure text sequence, allowing models to learn efficient text extraction without additional manual preprocessing steps.

Customizable Data Generation: The dataset is built using open-source generator code, which provides flexibility for users to adjust hundreds of parameters. It supports custom corpora, so users can replace the probabilistically generated “gibberish” text with actual texts relevant to their use cases.

Scalable and Efficient: Thanks to parallelized processing, larger datasets can be generated rapidly. Users with powerful computational resources can expand the dataset size to hundreds of thousands or even millions of pairs, making it an adaptable resource for large-scale AI training.

Multi-Script Support: The generator code can easily be extended to other scripts, including different Indic languages or even non-Indic languages, by modifying the Unicode character set and adjusting parameters such as sentence structure and paragraph lengths.

Dataset Applications:

This dataset is especially useful for developing OCR systems that handle Telugu language documents. However, the data generation process is flexible enough to extend to other Indic languages and non-Indic scripts, making it a versatile resource for cross-lingual and multi-modal research in text extraction, document understanding, and AI-driven translation.

This dataset is sourced from Kaggle.

Explicit Image Dataset 1

Explicit Image Dataset 1 (EXIM1) is a collection of user-generated content from a variety of publicly-availably sources across the internet. EXIM1's images primarily come from Reddit. Most, if not all of the state-of-the-art diffusion and image generation models come 'aligned' out-of-the-box. While AI safety is an important aspect of this profession, such alignment often results in less than ideal outputs from the model. The goal of this dataset is to… See the full description on the dataset page: https://huggingface.co/datasets/Interformed/EXIM1-3_RAW.

Crowd Counting Dataset

The dataset includes images featuring crowds of people ranging from 0 to 5000 individuals. The dataset includes a diverse range of scenes and scenarios, capturing crowds in various settings. Each image in the dataset is accompanied by a corresponding JSON file containing detailed labeling information for each person in the crowd for crowd count and classification.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4b51a212e59f575bd6978f215a32aca0%2FFrame%2064.png?generation=1701336719197861&alt=media" alt="">

Types of crowds in the dataset: 0-1000, 1000-2000, 2000-3000, 3000-4000 and 4000-5000

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F72e0fed3ad13826d6545ff75a79ed9db%2FFrame%2065.png?generation=1701337622225724&alt=media" alt="">

This dataset provides a valuable resource for researchers and developers working on crowd counting technology, enabling them to train and evaluate their algorithms with a wide range of crowd sizes and scenarios. It can also be used for benchmarking and comparison of different crowd counting algorithms, as well as for real-world applications such as public safety and security, urban planning, and retail analytics.

Full version of the dataset includes 647 labeled images of crowds, leave a request on TrainingData to buy the dataset

Statistics for the dataset (number of images by the crowd's size and image width):

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F2e9f36820e62a2ef62586fc8e84387e2%2FFrame%2063.png?generation=1701336725293625&alt=media" alt="">

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

Get the Dataset

This is just an example of the data

Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

• images - includes original images of crowds placed in subfolders according to its size,
• labels - includes json-files with labeling and visualised labeling for the images in the previous folder,
• csv file - includes information for each image in the dataset

File with the extension .csv

• id: id of the image,
• image: link to access the original image,
• label: link to access the json-file with labeling,
• type: type of the crowd on the photo

TrainingData provides high-quality data annotation tailored to your needs

keywords: crowd counting, crowd density estimation, people counting, crowd analysis, image annotation, computer vision, deep learning, object detection, object counting, image classification, dense regression, crowd behavior analysis, crowd tracking, head detection, crowd segmentation, crowd motion analysis, image processing, machine learning, artificial intelligence, ai, human detection, crowd sensing, image dataset, public safety, crowd management, urban planning, event planning, traffic management

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

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...

Description:

👉 Download the dataset here

The Linear Equation Image Dataset is designed to help solve high school-level algebraic problems using machine learning (ML). It provides extensive visual data, ideal for training models in equation recognition and solving.

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What’s New

Expanded Image Dataset: The dataset now contains over 30,000 images, covering a wide array of linear equations with varying complexities. The generation of equations follows multiple randomization techniques, ensuring diversity in the visual representation.

Data Diversity: Equations include both simple and complex forms, with some involving fractional coefficients, inequalities, or multi-variable formats to increase the challenge. The images also come in different resolutions, fonts, and formats (handwritten and digitally rendered) to further test ML algorithms’ robustness.

Possible Use Cases

Symbolic Equation Recognition: Train models to visually recognize equations and convert them into symbolic form.

Equation Solving: Create ML models capable of solving linear equations through image recognition.

Handwritten Recognition: Use this dataset for handwriting recognition, helping machines interpret handwritten linear equations.

Educational Tools: Develop AI tutors or mobile apps that assist students in solving linear equations by merely taking a photo of the problem.

Algorithm Training: Useful for those researching symbolic computation, this dataset allows for testing and improving various image-to-text and equation-solving algorithms.

Enhanced Research Opportunities

This dataset can be particularly useful for educational institutions, research teams, and AI developers focusing on enhancing problem-solving capabilities via machine learning and symbolic computation models.

This dataset is sourced from Kaggle.

Description:

👉 Download the dataset here

The Pix2Pix Facades dataset is widely used for image-to-image translation tasks, specifically in architectural and structural imagery. It supports the Pix2Pix Generative Adversarial Network (GAN), which excels at translating facade images (buildings) into segmented images. The Pix2Pix model, leveraging a deep convolutional architecture, is capable of generating high-resolution outputs (256×256 pixels and beyond) and is effective in various image-conditional translation tasks, such as style transfer, object rendering, and architectural visualization.

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Image-Conditional GAN

Pix2Pix uses a specialized GAN architecture, facilitating high-resolution (256×256 pixels) image generation for translation tasks such as facade segmentation.

Applications

This dataset is highly valuable in various fields, primarily in building segmentation, urban planning, and architectural design. It provides essential annotations that help AI models distinguish different elements of building facades, enhancing accuracy in image processing tasks. In urban planning, the dataset aids in creating automated tools for city structure analysis, helping architects and planners visualize potential changes to urban landscapes.

Advanced Use

Beyond architecture, the Pix2Pix Facades dataset extends its utility across a wide range of image-to-image translation tasks. Researchers and developers can leverage this dataset for applications in medical imaging (e.g., converting CT scans into segmented views), satellite imagery (transforming raw satellite data into readable maps), and even fashion (translating sketches into finished designs). Its flexibility in handling various visual translation problems makes it an invaluable tool for advancing AI solutions in fields like autonomous driving, augmented reality, and content generation.

This dataset is sourced from Kaggle.

Dataset Card for t2i_tiny_nasa

  NASA Image Dataset

This dataset is created using images obtained from NASA's official image library. The dataset contains a collection of images along with their corresponding textual descriptions (prompts). This dataset can be used for various applications, including image-to-text tasks, text-to-image generation, and other AI-based image analysis studies.

  Dataset Information

Source: NASA Image Library Content: Images and… See the full description on the dataset page: https://huggingface.co/datasets/kaangml/t2i_tiny_nasa.

Overview

The Hard Hat dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.

The original dataset has a 75/25 train-test split.

Example Image: https://i.imgur.com/7spoIJT.png" alt="Example Image">

Use Cases

One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.

Using this Dataset

Use the fork or Download this Dataset button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

Dataset Versions:

Image Preprocessing | Image Augmentation | Modify Classes * v1 (resize-416x416-reflect): generated with the original 75/25 train-test split | No augmentations * v2 (raw_75-25_trainTestSplit): generated with the original 75/25 train-test split | These are the raw, original images * v3 (v3): generated with the original 75/25 train-test split | Modify Classes used to drop person class | Preprocessing and Augmentation applied * v5 (raw_HeadHelmetClasses): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class * v8 (raw_HelmetClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and person classes * v9 (raw_PersonClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and helmet classes * v10 (raw_AllClasses): generated with a 70/20/10 train/valid/test split | These are the raw, original images * v11 (augmented3x-AllClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied | 3x image generation | Trained with Roboflow's Fast Model * v12 (augmented3x-HeadHelmetClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Fast Model * v13 (augmented3x-HeadHelmetClasses-AccurateModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Accurate Model * v14 (raw_HeadClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class, and remap/relabel helmet class to head

Choosing Between Computer Vision Model Sizes | Roboflow Train

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Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

NeSy4VRD

NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the computer vision field of AI and, within that field, to the application tasks of visual relationship detection (VRD) and scene graph generation.

Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind). This is the sense in which we describe NeSy4VRD as being multipurpose: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives.

The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.

NeSy4VRD on Zenodo: the NeSy4VRD dataset package

This entry on Zenodo hosts the NeSy4VRD dataset package, which includes the NeSy4VRD dataset and its companion NeSy4VRD ontology, an OWL ontology called VRD-World.

The NeSy4VRD dataset consists of an image dataset with associated visual relationship annotations. The images of the NeSy4VRD dataset are the same as those that were once publicly available as part of the VRD dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations. The NeSy4VRD dataset is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects. A visual relationship for an image of the NeSy4VRD dataset has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them. Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes. For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>.

Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation. Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing.

The NeSy4VRD ontology, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the NeSy4VRD dataset. It directly describes the domain of the NeSy4VRD dataset, as reflected in the NeSy4VRD visual relationship annotations. More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components. There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning.

Use of the NeSy4VRD ontology, VRD-World, in conjunction with the NeSy4VRD dataset is, of course, purely optional, however. Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the NeSy4VRD ontology and use the NeSy4VRD dataset by itself.

All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.

NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code

The NeSy4VRD research resource incorporates additional components that are companions to the NeSy4VRD dataset package here on Zenodo. These companion components are available at NeSy4VRD on GitHub. These companion components consist of:

• comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the NeSy4VRD ontology, VRD-World, as well)
• open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the NeSy4VRD ontology, VRD-World, and RDF knowledge graphs.

The NeSy4VRD infrastructure supporting extensibility consists of:

• open source Python code for conducting deep and comprehensive analyses of the NeSy4VRD dataset (the VRD images and their associated NeSy4VRD visual relationship annotations)
• an open source, custom-designed NeSy4VRD protocol for specifying visual relationship annotation customisation instructions declaratively, in text files
• an open source, custom-designed NeSy4VRD workflow, implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.

The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the NeSy4VRD dataset in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests. The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned.

The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the NeSy4VRD ontology, VRD-World, in conjunction with the NeSy4VRD dataset. These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer. If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained.

To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between thing objects (that have well-defined shapes) and stuff objects (that are amorphous). Suppose a researcher wishes to have a greater number of stuff object classes with which to work. Water is such a stuff object. Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a Water class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following:

• use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);
• use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;
• use the NeSy4VRD protocol to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);
• use the NeSy4VRD workflow to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;
• introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);
• continue experimenting, now with the added benefit of the additional stuff object class 'water';
• contribute the enriched set of NeSy4VRD visual relationship

🛡️ Aegis AI Content Safety Dataset 1.0

Aegis AI Content Safety Dataset is an open-source content safety dataset (CC-BY-4.0), which adheres to Nvidia's content safety taxonomy, covering 13 critical risk categories (see Dataset Description).

  Dataset Details







  Dataset Description

The Aegis AI Content Safety Dataset is comprised of approximately 11,000 manually annotated interactions between humans and LLMs, split into 10,798 training samples and 1,199… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-1.0.

Final resampling generator models produced from the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) (https://github.com/xinntao/ESRGAN). ESRGAN was trained at two different resampling factors, 4x and 10x, using a training data set of global Planet CubeSat satellite images. These generators can be used to resample Planet CubeSat satellite images from 30m and 12m to 3m resolution. Descriptions and results of training can be found at https://wandb.ai/elezine/pixelsmasher. In press at Canadian Journal of Remote Sensing: Super-resolution surface water mapping on the Canadian Shield using Planet CubeSat images and a Generative Adversarial Network, Ekaterina M. D. Lezine, Ethan D. Kyzivat, and Laurence C. Smith (2021).

The Abstractive News Captions with High-level cOntext Representation (ANCHOR) dataset contains 70K+ samples sourced from 5 different news media organizations. This dataset can be utilized for Vision & Language tasks such as Text-to-Image Generation, Image Caption Generation, etc.

The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US. The ecological effects of wildland fire � also termed the fire severity � are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role. Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature. In turn, these statistical models were used to generate 'wall-to-wall' maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.

Snapshot img

Synthetic Data Generation Market Size 2024-2028

The synthetic data generation market size is forecast to increase by USD 2.88 billion at a CAGR of 60.02% between 2023 and 2028.

The global synthetic data generation market is expanding steadily, driven by the growing need for privacy-compliant data solutions and advancements in AI technology. Key factors include the increasing demand for data to train machine learning models, particularly in industries like healthcare services and finance where privacy regulations are strict and the use of predictive analytics is critical, and the use of generative AI and machine learning algorithms, which create high-quality synthetic datasets that mimic real-world data without compromising security.
This report provides a detailed analysis of the global synthetic data generation market, covering market size, growth forecasts, and key segments such as agent-based modeling and data synthesis. It offers practical insights for business strategy, technology adoption, and compliance planning. A significant trend highlighted is the rise of synthetic data in AI training, enabling faster and more ethical development of models. One major challenge addressed is the difficulty in ensuring data quality, as poorly generated synthetic data can lead to inaccurate outcomes.
For businesses aiming to stay competitive in a data-driven global landscape, this report delivers essential data and strategies to leverage synthetic data trends and address quality challenges, ensuring they remain leaders in innovation while meeting regulatory demands

What will be the Size of the Market During the Forecast Period?

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Synthetic data generation offers a more time-efficient solution compared to traditional methods of data collection and labeling, making it an attractive option for businesses looking to accelerate their AI and machine learning projects. The market represents a promising opportunity for organizations seeking to overcome the challenges of data scarcity and privacy concerns while maintaining data diversity and improving the efficiency of their artificial intelligence and machine learning initiatives. By leveraging this technology, technology decision-makers can drive innovation and gain a competitive edge in their respective industries.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Healthcare and life sciences
  Retail and e-commerce
  Transportation and logistics
  IT and telecommunication
  BFSI and others


Type

  Agent-based modelling
  Direct modelling


Data

  Tabular Data
  Text Data
  Image & Video Data
  Others


Offering Band

  Fully Synthetic Data
  Partially Synthetic Data
  Hybrid Synthetic Data


Application

  Data Protection
  Data Sharing
  Predictive Analytics
  Natural Language Processing
  Computer Vision Algorithms
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    Japan
    India


  Middle East and Africa



  South America

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period. In the thriving healthcare and life sciences sector, synthetic data generation is gaining significant traction as a cost-effective and time-efficient alternative to utilizing real-world data. This market segment's rapid expansion is driven by the increasing demand for data-driven insights and the importance of safeguarding sensitive information. One noteworthy application of synthetic data generation is in the realm of computer vision, specifically with geospatial imagery and medical imaging.

For instance, in healthcare, synthetic data can be generated to replicate medical imaging, such as MRI scans and X-rays, for research and machine learning model development without compromising patient privacy. Similarly, in the field of physical security, synthetic data can be employed to enhance autonomous vehicle simulation, ensuring optimal performance and safety without the need for real-world data. By generating artificial datasets, organizations can diversify their data sources and improve the overall quality and accuracy of their machine learning models.

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The healthcare and life sciences segment was valued at USD 12.60 million in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the m

Dataset Title

Skin Disease GAN-Generated Lightweight Dataset

General Description

This dataset is a collection of skin disease images generated using a Generative Adversarial Network (GAN) approach. Specifically, a GAN was utilized with Stable Diffusion as the generator and a transformer-based discriminator to create realistic images of various skin diseases. The GAN approach enhances the accuracy and realism of the generated images, making this dataset a valuable resource for machine learning and computer vision applications in dermatology.

Creation Process

To create this dataset, a series of Low-Rank Adaptations (LoRAs) were generated for each disease category. These LoRAs were trained on the base dataset with 60 epochs and 30,000 steps using OneTrainer. Images were then generated for the following disease categories:

• Herpes
• Measles
• Chickenpox
• Monkeypox

Due to the availability of ample public images, Melanoma was excluded from the generation process. The Fooocus API served as the generator within the GAN framework, creating images based on the LoRAs.

To ensure quality and accuracy, a transformer-based discriminator was employed to verify the generated images, classifying them into the correct disease categories.

Sources

The original base dataset used to create this GAN-based dataset includes reputable sources such as:

2019 HAM10000 Challenge - Kaggle - Google Images - Dermnet NZ - Bing Images - Yandex - Hellenic Atlas - Dermatological Atlas The LoRAs and their recommended weights for generating images are available for download on our CivitAi profile. You can refer to this profile for detailed instructions and access to the LoRAs used in this dataset.

Dataset Contents

Generated Images: High-quality images of skin diseases generated via GAN with Stable Diffusion, using transformer-based discrimination for accurate classification.

Categories

• Herpes
• Measles
• Chickenpox
• Monkeypox Each image corresponds to one of these four categories, providing a reliable set of generated data for training and evaluation. Melanoma was excluded from generation due to the abundance of public data.

Suggested Use Cases

This dataset is suitable for:

• Image Classification and Augmentation Tasks: Training and evaluating models in skin disease classification, with additional augmentation from generated images.
• Research in Dermatology and GAN Techniques: Investigating the effectiveness of GANs for generating medical images, as well as exploring the use of transformer-based discrimination.
• Educational Projects in AI and Medicine: Offering insights into image generation for diagnostic purposes, combining GANs and Stable Diffusion with transformers for medical datasets.

Citation

When using this dataset, please cite the following reference: Espinosa, E.G., Castilla, J.S.R., Lamont, F.G. (2025). Skin Disease Pre-diagnosis with Novel Visual Transformers. In: Figueroa-García, J.C., Hernández, G., Suero Pérez, D.F., Gaona García, E.E. (eds) Applied Computer Sciences in Engineering. WEA 2024. Communications in Computer and Information Science, vol 2222. Springer, Cham. https://doi.org/10.1007/978-3-031-74595-9_10