OverviewThis is a set of synthetic overhead imagery of wind turbines that was created with CityEngine. There are corresponding labels that provide the class, x and y coordinates, and height and width (YOLOv3 format) of the ground truth bounding boxes for each wind turbine in the images. These labels are named similarly to the images (e.g. image.png will have the label titled image.txt)..UseThis dataset is meant as supplementation to training an object detection model on overhead images of wind turbines. It can be added to the training set of an object detection model to potentially improve performance when using the model on real overhead images of wind turbines.WhyThis dataset was created to examine the utility of adding synthetic imagery to the training set of an object detection model to improve performance on rare objects. Since wind turbines are both very rare in number and sparse, this makes acquiring data very costly. This synthetic imagery is meant to solve this issue by automating the generation of new training data. The use of synthetic imagery can also be applied to the issue of cross-domain testing, where the model lacks training data on a particular region and consequently struggles when used on that region.MethodThe process for creating the dataset involved selecting background images from NAIP imagery available on Earth OnDemand. These images were randomlyselected from these geographies: forest, farmland, grasslands, water, urban/suburban,mountains, and deserts. No consideration was put into whether the background images would seem realistic. This is because we wanted to see if this would help the model become better at detecting wind turbines regardless of their context (which would help when using the model on novel geographies). Then, a script was used to select these at random and uniformly generate 3D models of large wind turbines over the image and then position the virtual camera to save four 608x608 pixel images. This process was repeated with the same random seed, but with no background image and the wind turbines colored as black. Next, these black and white images were converted into ground truth labels by grouping the black pixels in the images.

Example computer vision classification training data derived from British Library 19th Century Books Image collection

This dataset provides training data for image classification for use in a computer vision workshop. The images are derived from 'Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG' from the year '1839'.

Currently, included are four folders containing a variety of images derived from the BL books corpus.

'cv_workshop_exercise_data' include images of: 'building', 'people', 'coat of arms''humancats' contains images of humans and images of catsThe 'fashion' and 'portraits' folders both contain images of people organised into 'female' and 'male'. These labels were annotated by a single annotator and these categories may themselves not be meaningful. They are included in the workshop data as a point of discussion about how we should label data both in general and when working with historical data.

This data is intended primarily as an educational resource.

Explore and download labeled image datasets for AI, ML, and computer vision. Find datasets for object detection, image classification, and image segmentation.

Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.

1. Specifications Data size : 10,000 ID

Race distribution : Asian, Caucasian, Black, Brown

Gender distribution : male, female

Age distribution : from teenagers to the elderly, mainly young and middle-aged

Collection environment : indoor office scenes, in-car,conference, etc.

Collection diversity : different gestures data, different races, different age groups, different scenes

Collection equipment : cellphone, laptop camera, in-car camera

Data format : .mp4, .mov, .jpg

Accuracy rate : the accuracy exceeds 97% based on the accuracy of the actions; the accuracy of action naming is more than 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go machine learning (ML) data supports instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

Annotated Imagery Data

FileMarket provides a robust Annotated Imagery Data set designed to meet the diverse needs of various computer vision and machine learning tasks. This dataset is part of our extensive offerings, which also include Textual Data, Object Detection Data, Large Language Model (LLM) Data, and Deep Learning (DL) Data. Each category is meticulously crafted to ensure high-quality and comprehensive datasets that empower AI development.

Specifications:

Data Size: 50,000 images Collection Environment: The images cover a wide array of real-world scenarios, including shop signs, stop boards, posters, tickets, road signs, comics, cover pictures, prompts/reminders, warnings, packaging instructions, menus, building signs, and more. Diversity: The dataset spans 5 languages and includes images from various natural scenes captured at multiple photographic angles (looking up, looking down, eye-level). Devices Used: Images are captured using cellphones and cameras, reflecting real-world usage. Image Parameters: All images are provided in .jpg format, and the corresponding annotation files are in .json format. Annotation Details: The dataset includes line-level quadrilateral bounding box annotations and text transcriptions. Accuracy: The error margin for each vertex of the quadrilateral bounding box is within 5 pixels, ensuring bounding box accuracy of at least 97%. The text transcription accuracy also meets or exceeds 97%. Unique Data Collection Method: FileMarket utilizes a community-driven approach to collect data, leveraging our extensive network of over 700k users across various Telegram apps. This method ensures that our datasets are diverse, real-world applicable, and ethically sourced, with full participant consent. This approach allows us to provide datasets that are both comprehensive and reflective of real-world scenarios, ensuring that your AI models are trained on the most relevant and diverse data available.

By integrating our unique data collection method with the specialized categories we offer, FileMarket is committed to providing high-quality data solutions that support and enhance your AI and machine learning projects.

Dy

Off-the-shelf gesture recognition data covers multiple scenes, such as conference, in-car and home. All the machine learning (ML) data is collected with signed authorization agreement.

Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient Image Classification", https://arxiv.org/abs/1511.06744

Model names differ from those in the paper, but the csv files for each set of experiments relates the paper's name for the model and the real name of the model here:

• cifarma.csv: Network-in-Network CIFAR10 Models
• mitma.csv: MIT Places Models
• googlenetma.csv: GoogLeNet ILSVRC2012 Models
• vggma.csv: VGG-11 ILSVRC2012 Models

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

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

💴 Buy 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

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

The computer vision products market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated market value of approximately $45 billion by 2033. This expansion is fueled by several key factors, including advancements in artificial intelligence (AI) and machine learning (ML), enabling more sophisticated image and video analysis capabilities. The rising demand for automation in manufacturing, healthcare, and automotive industries is another significant driver. Furthermore, the decreasing cost of hardware components, particularly sensors and processors, is making computer vision technology more accessible to a wider range of businesses and applications. Key trends include the growing integration of computer vision with cloud computing for enhanced data processing and storage, the proliferation of edge computing for real-time applications, and the increasing development of specialized computer vision solutions for specific industry needs, such as autonomous vehicles and advanced medical imaging. Despite the significant growth potential, certain restraints exist. These include concerns regarding data privacy and security, the need for high-quality training data for accurate AI models, and the complexity of integrating computer vision systems into existing infrastructures. However, continuous advancements in technology and the increasing awareness of the benefits of computer vision are expected to mitigate these challenges. Major players such as Baumer Optronic, Omron Corporation, Sick AG, and others are actively investing in research and development to enhance product capabilities and expand market reach, fostering a highly competitive yet dynamic market landscape. Segmentation within the market is likely driven by application (e.g., industrial automation, medical imaging, security), technology (e.g., 3D vision, 2D vision), and deployment (e.g., on-premise, cloud-based).

Explore our Trucks Detection Dataset, featuring 746 annotated images ideal for training machine learning models.

Unlock the potential of Tesseract OCR with our meticulously hand-labeled training dataset. Designed for fine-tuning, this dataset includes comprehensive files and a custom Bash script to streamline your OCR improvements.

Explore the Synthetic Rock Paper Scissors Dataset featuring a diverse collection of augmented images for training and testing machine learning models.

This dataset contains 4,599 high-quality, annotated images of 25 commonly used chemistry lab apparatuses. The images, each containing structures in real-world settings, have been captured from different angles, backgrounds, and distances, while also undergoing variations in lighting to aid in the robustness of object detection models. Every image has been labeled using bounding box annotation in YOLO and COCO format, alongside the class IDs and normalized bounding box coordinates making object detection more precise. The annotations and bounding boxes have been built using the Roboflow platform.To achieve a better learning procedure, the dataset has been split into three sub-datasets: training, validation, and testing. The training dataset constitutes 70% of the entire dataset, with validation and testing at 20% and 10% respectively. In addition, all images undergo scaling to a standard of 640x640 pixels while being auto-oriented to rectify rotation discrepancies brought about by the EXIF metadata. The dataset is structured in three main folders - train, valid, and test, and each contains images/ and labels/ subfolders. Every image contains a label file containing class and bounding box data corresponding to each detected object.The whole dataset features 6,960 labeled instances per 25 apparatus categories including beakers, conical flasks, measuring cylinders, test tubes, among others. The dataset can be utilized for the development of automation systems, real-time monitoring and tracking systems, tools for safety monitoring, alongside AI educational tools.

Wirestock's AI/ML Image Training Data, 4.5M Files with Metadata: This data product is a unique offering in the realm of AI/ML training data. What sets it apart is the sheer volume and diversity of the dataset, which includes 4.5 million files spanning across 20 different categories. These categories range from Animals/Wildlife and The Arts to Technology and Transportation, providing a rich and varied dataset for AI/ML applications.

The data is sourced from Wirestock's platform, where creators upload and sell their photos, videos, and AI art online. This means that the data is not only vast but also constantly updated, ensuring a fresh and relevant dataset for your AI/ML needs. The data is collected in a GDPR-compliant manner, ensuring the privacy and rights of the creators are respected.

The primary use-cases for this data product are numerous. It is ideal for training machine learning models for image recognition, improving computer vision algorithms, and enhancing AI applications in various industries such as retail, healthcare, and transportation. The diversity of the dataset also means it can be used for more niche applications, such as training AI to recognize specific objects or scenes.

This data product fits into Wirestock's broader data offering as a key resource for AI/ML training. Wirestock is a platform for creators to sell their work, and this dataset is a collection of that work. It represents the breadth and depth of content available on Wirestock, making it a valuable resource for any company working with AI/ML.

The core benefits of this dataset are its volume, diversity, and quality. With 4.5 million files, it provides a vast resource for AI training. The diversity of the dataset, spanning 20 categories, ensures a wide range of images for training purposes. The quality of the images is also high, as they are sourced from creators selling their work on Wirestock.

In terms of how the data is collected, creators upload their work to Wirestock, where it is then sold on various marketplaces. This means the data is sourced directly from creators, ensuring a diverse and unique dataset. The data includes both the images themselves and associated metadata, providing additional context for each image.

The different image categories included in this dataset are Animals/Wildlife, The Arts, Backgrounds/Textures, Beauty/Fashion, Buildings/Landmarks, Business/Finance, Celebrities, Education, Emotions, Food Drinks, Holidays, Industrial, Interiors, Nature Parks/Outdoor, People, Religion, Science, Signs/Symbols, Sports/Recreation, Technology, Transportation, Vintage, Healthcare/Medical, Objects, and Miscellaneous. This wide range of categories ensures a diverse dataset that can cater to a variety of AI/ML applications.

Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological innovation, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by the digital transformation of emerging economies and the proliferation of AI applications across various industry sectors.

Data Type Analysis

The AI training dataset market is segmented by data type into Text, Image/Video, Audio, and Others, each playing a crucial role in powering different AI applications. Text da

Dataset of 3623 images from 1661 users (~2.18/user), mainly front/back ID documents, ideal for OCR training, document recognition, and automated identity verification tasks.

Artificial Intelligence-based image generation has recently seen remarkable advancements, largely driven by deep learning techniques, such as Generative Adversarial Networks (GANs). With the influx and development of generative models, so too have biometric re-identification models and presentation attack detection models seen a surge in discriminative performance. However, despite the impressive photo-realism of generated samples and the additive value to the data augmentation pipeline, the role and usage of machine learning models has received intense scrutiny and criticism, especially in the context of biometrics, often being labeled as untrustworthy. Problems that have garnered attention in modern machine learning include: humans' and machines' shared inability to verify the authenticity of (biometric) data, the inadvertent leaking of private biometric data through the image synthesis process, and racial bias in facial recognition algorithms. Given the arrival of these unwanted side effects, public trust has been shaken in the blind use and ubiquity of machine learning.

However, in tandem with the advancement of generative AI, there are research efforts to re-establish trust in generative and discriminative machine learning models. Explainability methods based on aggregate model salience maps can elucidate the inner workings of a detection model, establishing trust in a post hoc manner. The CYBORG training strategy, originally proposed by Boyd, attempts to actively build trust into discriminative models by incorporating human salience into the training process.

In doing so, CYBORG-trained machine learning models behave more similar to human annotators and generalize well to unseen types of synthetic data. Work in this dissertation also attempts to renew trust in generative models by training generative models on synthetic data in order to avoid identity leakage in models trained on authentic data. In this way, the privacy of individuals whose biometric data was seen during training is not compromised through the image synthesis procedure. Future development of privacy-aware image generation techniques will hopefully achieve the same degree of biometric utility in generative models with added guarantees of trustworthiness.

The global image recognition software market, currently valued at $2568.3 million (2025), is poised for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of artificial intelligence (AI) across diverse sectors, including healthcare, retail, and security, is a primary catalyst. Automated image analysis significantly improves efficiency and accuracy in various tasks, from medical diagnosis to fraud detection. Furthermore, advancements in deep learning algorithms and the availability of vast amounts of labeled image data are fueling the development of more sophisticated and accurate image recognition solutions. The rise of cloud-based solutions, offering scalability and cost-effectiveness, also contributes to market growth. Competition among major players like Microsoft, AWS, Google, and IBM further stimulates innovation and lowers prices, making the technology accessible to a wider range of businesses. However, challenges remain, including concerns over data privacy and security, the need for high-quality training data, and the potential for bias in algorithms. Market segmentation reveals significant opportunities within specific application areas. Large enterprises are currently the leading adopters, leveraging image recognition for improved operational efficiency and strategic decision-making. However, the growing adoption of AI by SMEs presents a substantial untapped market segment ripe for expansion. Geographically, North America currently holds a significant market share, driven by strong technological advancements and early adoption. However, Asia Pacific is projected to experience the most rapid growth due to the increasing digitalization and investment in AI across several developing economies like India and China. The on-premises deployment model remains prevalent, but cloud-based solutions are gaining traction due to their flexibility and reduced infrastructure costs. The market's future trajectory will depend heavily on ongoing advancements in algorithm development, the resolution of ethical concerns, and the expansion of affordable and accessible solutions.