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.

This dataset contains metadata related to three categories of AI and computer vision applications:

Handwritten Math Solutions: Metadata on images of handwritten math problems with step-by-step solutions.

Multi-lingual Street Signs: Road sign images in various languages, with translations.

Security Camera Anomalies: Surveillance footage metadata distinguishing between normal and suspicious activities.

The dataset is useful for machine learning, image recognition, OCR (Optical Character Recognition), anomaly detection, and AI model training.

Synthetic Data for Computer Vision Market Outlook

According to our latest research, the global synthetic data for computer vision market size reached USD 420 million in 2024, with a robust year-over-year growth underpinned by the surging demand for advanced AI-driven visual systems. The market is expected to expand at a compelling CAGR of 34.2% from 2025 to 2033, culminating in a forecasted market size of approximately USD 4.9 billion by 2033. This accelerated growth is primarily driven by the increasing adoption of synthetic data to overcome data scarcity, privacy concerns, and the need for scalable, diverse datasets to train computer vision models efficiently and ethically.

The primary growth factor fueling the synthetic data for computer vision market is the exponential rise in AI and machine learning applications across various industries. As organizations strive to enhance their computer vision systems, the demand for large, annotated, and diverse datasets has become paramount. However, acquiring real-world data is often expensive, time-consuming, and fraught with privacy and regulatory challenges. Synthetic data, generated through advanced simulation and rendering techniques, addresses these issues by providing high-quality, customizable datasets that can be tailored to specific use cases. This not only accelerates the training of AI models but also significantly reduces costs and mitigates the risks associated with sensitive data, making it an indispensable tool for enterprises seeking to innovate rapidly.

Another significant driver is the rapid advancement of simulation technologies and generative AI models, such as GANs (Generative Adversarial Networks), which have dramatically improved the realism and utility of synthetic data. These technologies enable the creation of highly realistic images, videos, and 3D point clouds that closely mimic real-world scenarios. As a result, industries such as automotive (for autonomous vehicles), healthcare (for medical imaging), and security & surveillance are leveraging synthetic data to enhance the robustness and accuracy of their computer vision systems. The ability to generate rare or dangerous scenarios that are difficult or unethical to capture in real life further amplifies the value proposition of synthetic data, driving its adoption across safety-critical domains.

Furthermore, the growing emphasis on data privacy and regulatory compliance, especially in regions with stringent data protection laws like Europe and North America, is propelling the adoption of synthetic data solutions. By generating artificial datasets that do not contain personally identifiable information, organizations can sidestep many of the legal and ethical hurdles associated with using real-world data. This is particularly relevant in sectors such as healthcare and retail, where data sensitivity is paramount. As synthetic data continues to gain regulatory acceptance and technological maturity, its role in supporting compliant, scalable, and bias-mitigated AI development is expected to expand significantly, further boosting market growth.

Synthetic Training Data is becoming increasingly vital in the realm of AI development, particularly for computer vision applications. By leveraging synthetic training data, developers can create expansive and diverse datasets that are not only cost-effective but also free from the biases often present in real-world data. This approach allows for the simulation of numerous scenarios and conditions, providing a robust foundation for training AI models. As a result, synthetic training data is instrumental in enhancing the accuracy and reliability of computer vision systems, making it an indispensable tool for industries aiming to innovate and improve their AI-driven solutions.

Regionally, North America currently leads the synthetic data for computer vision market, driven by the presence of major technology companies, robust R&D investments, and early adoption across key industries. However, Asia Pacific is emerging as a high-growth region, fueled by rapid industrialization, expanding AI research ecosystems, and increasing government support for digital transformation initiatives. Europe also exhibits strong momentum, underpinned by a focus on privacy-preserving AI solutions and regulatory compliance. Collectively, these regional trends underscore a global sh

Computer Vision YOLOv8 Training

## Overview

Computer Vision YOLOv8 Training is a dataset for object detection tasks - it contains COTS Fish Coral annotations for 637 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Vehicle Detection Dataset

This dataset is designed for vehicle detection tasks, featuring a comprehensive collection of images annotated for object detection. This dataset, originally sourced from Roboflow (https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system), was exported on May 29, 2025, at 4:59 PM GMT and is now publicly available on Kaggle under the CC BY 4.0 license.

Overview

• Purpose: The dataset supports the development of computer vision models for detecting various types of vehicles in traffic scenarios.
• Classes: The dataset includes annotations for 7 vehicle types:
  • Bicycle
  • Bus
  • Car
  • Motorbike
  • Rickshaw
  • Truck
  • Van
• Number of Images: The dataset contains 9,440 images, split into training, validation, and test sets:
  • Training: Images located in ../train/images
  • Validation: Images located in ../valid/images
  • Test: Images located in ../test/images
• Annotation Format: Images are annotated in YOLOv11 format, suitable for training state-of-the-art object detection models.
• Pre-processing: Each image has been resized to 640x640 pixels (stretched). No additional image augmentation techniques were applied.

Source and Creation

This dataset was created and exported via Roboflow, an end-to-end computer vision platform that facilitates collaboration, image collection, annotation, dataset creation, model training, and deployment. The dataset is part of the ai-traffic-system project (version 1) under the workspace object-detection-sn8ac. For more details, visit: https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system/dataset/1.

Usage

This dataset is ideal for researchers, data scientists, and developers working on vehicle detection and traffic monitoring systems. It can be used to: - Train and evaluate deep learning models for object detection, particularly using the YOLOv11 framework. - Develop AI-powered traffic management systems, autonomous driving applications, or urban mobility solutions. - Explore computer vision techniques for real-world traffic scenarios.

For advanced training notebooks compatible with this dataset, check out: https://github.com/roboflow/notebooks. To explore additional datasets and pre-trained models, visit: https://universe.roboflow.com.

License

The dataset is licensed under CC BY 4.0, allowing for flexible use, sharing, and adaptation, provided appropriate credit is given to the original source.

This dataset is a valuable resource for building robust vehicle detection models and advancing computer vision applications in traffic systems.

3DIFICE: 3-dimensional Damage Imposed on Frame structures for Investigating Computer vision-based Evaluation methods This dataset contains 1,396 synthetic images and label maps with various types of earthquake damage imposed on reinforced concrete frame structures. Damage includes: cracking, spalling, exposed transverse rebar, and exposed longitudinal rebar. Each image has an associated label map that can be used for training machine learning algorithms to recognize the various types of damage.

The dataset is structured for person object detection tasks, containing separate directories for training, validation, and testing. Each split has an images folder with corresponding images and a labels folder with annotation files.

Train Set: Contains images and annotations for model training.

Validation Set: Includes images and labels for model evaluation during training.

Test Set: Provides unseen images and labels for final model performance assessment.

Each annotation file (TXT format) corresponds to an image and likely contains bounding box coordinates and class labels. This structure follows standard object detection dataset formats, ensuring easy integration with detection models like yolo,RT-DETR.

Dataset Structure

📂 dataset/ ├── 📁 train/ │ ├── 📂 images/ │ │ ├── 🖼 image1.jpg (Training image) │ │ ├── 🖼 image2.jpg (Training image) │ ├── 📂 labels/ │ │ ├── 📄 image1.txt (Annotation for image1.jpg) │ │ ├── 📄 image2.txt (Annotation for image2.jpg) │ ├── 📁 val/ │ ├── 📂 images/ │ │ ├── 🖼 image3.jpg (Validation image) │ │ ├── 🖼 image4.jpg (Validation image) │ ├── 📂 labels/ │ │ ├── 📄 image3.txt (Annotation for image3.jpg) │ │ ├── 📄 image4.txt (Annotation for image4.jpg) │ ├── 📁 test/ │ ├── 📂 images/ │ │ ├── 🖼 image5.jpg (Test image) │ │ ├── 🖼 image6.jpg (Test image) │ ├── 📂 labels/ │ │ ├── 📄 image5.txt (Annotation for image5.jpg) │ │ ├── 📄 image6.txt (Annotation for image6.jpg)

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

The booming video annotation service market for machine learning is projected to reach $10B+ by 2033, driven by AI adoption and the need for accurate training data. Explore market trends, key players (iMerit, HabileData, etc.), and growth opportunities in this comprehensive analysis.

Overview

This dataset contains sample synthetic data used for training a solution for reading analog pressure gauges values. We have used this during the writing of our paper and blog(s) which showcase how synthetic data can be used to train and use computer vision models. We've chosen the topic of Analog Gauge Reading Understanding as it is a common problem in many industries and exemplifies how output from multiple models can be consumed in heuristics to get a final reading.

Dataset contents

The dataset contains the following: - subset of the synthetic data used for training, we have included the two latest versions of datasets. Each contains both the images and the coco annotations for segmentation and pose estimation. - inference data for the test videos available in the Kaggle dataset. For each video there is one CSV file which contains for every frame the bbox for the (main) gauge, keypoints locations for the needle tip, gauge center, min and max scale ticks, and the predicted reading.

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 TXT (YOLO) 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.

The FSOCO dataset helps Formula Student / FSAE teams to get started with their visual perception system for driverless disciplines. State-of-the-art object detection systems require a substantial amount of data, which can be a challenge for new teams. We aim to overcome this problem by providing data and to help experienced teams to even further boost their performance on the track with an increased set of ground truth data.

FSOCO contains bounding box and segmentation annotations from multiple teams and continues to grow thanks to numerous contributions from the Formula Student community.

Labeled Lacrosse images suitable for training and evaluating computer vision and deep learning models.

Dataset Description: Human Faces and Objects Dataset (HFO-5000) The Human Faces and Objects Dataset (HFO-5000) is a curated collection of 5,000 images, categorized into three distinct classes: male faces (1,500), female faces (1,500), and objects (2,000). This dataset is designed for machine learning and computer vision applications, including image classification, face detection, and object recognition. The dataset provides high-quality, labeled images with a structured CSV file for seamless integration into deep learning pipelines.

Column Description: The dataset is accompanied by a CSV file that contains essential metadata for each image. The CSV file includes the following columns: file_name: The name of the image file (e.g., image_001.jpg). label: The category of the image, with three possible values: "male" (for male face images) "female" (for female face images) "object" (for images of various objects) file_path: The full or relative path to the image file within the dataset directory.

Uniqueness and Key Features: 1) Balanced Distribution: The dataset maintains an even distribution of human faces (male and female) to minimize bias in classification tasks. 2) Diverse Object Selection: The object category consists of a wide variety of items, ensuring robustness in distinguishing between human and non-human entities. 3) High-Quality Images: The dataset consists of clear and well-defined images, suitable for both training and testing AI models. 4) Structured Annotations: The CSV file simplifies dataset management and integration into machine learning workflows. 5) Potential Use Cases: This dataset can be used for tasks such as gender classification, facial recognition benchmarking, human-object differentiation, and transfer learning applications.

Conclusion: The HFO-5000 dataset provides a well-structured, diverse, and high-quality set of labeled images that can be used for various computer vision tasks. Its balanced distribution of human faces and objects ensures fairness in training AI models, making it a valuable resource for researchers and developers. By offering structured metadata and a wide range of images, this dataset facilitates advancements in deep learning applications related to facial recognition and object classification.

The Intelligent Training Data Service market is booming, projected to reach $10 billion by 2033 with a 25% CAGR. Learn about key drivers, trends, and leading companies shaping this rapidly evolving sector of AI development. Explore market segments like autonomous driving and robotics, and discover the impact of synthetic data generation.

Labeled Stop sign images suitable for training and evaluating computer vision and deep learning models.

This dataset includes 76,117 high-resolution images of liquid stains captured from the perspective of robotic cleaners in various indoor environments. The dataset features rich diversity in terms of room types, lighting conditions, angles, stain categories, and time of day. Designed for tasks such as stain detection, floor condition analysis, and robotic vision model training, this data helps improve perception and navigation capabilities for cleaning robots and other autonomous indoor systems. Suitable for machine learning, computer vision research, and commercial cleaning AI applications.

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.

The AI Training Data market is booming, projected to reach $89.4 Billion by 2033, with a CAGR of 25%. This comprehensive analysis explores market drivers, trends, restraints, key players (Google, Amazon, Microsoft), and regional breakdowns. Discover the future of AI data and its impact on various industries.

1. Overview This dataset is a collection of 30,000+ images of Face ID + 106 key points facial landmark that are ready to use for optimizing the accuracy of computer vision models. Images in the dataset includes People image with specific requirements as follow:
2. Age: above 20
3. Race: various

4. Angle: no more than 90 degree All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos.

5. Annotated Imagery Data of Face ID + 106 key points facial landmark This dataset contains 30,000+ images of Face ID + 106 key points facial landmark. The dataset has been annotated in - face bounding box, Attribute of race, gender, age, skin tone and 106 keypoints facial landmark. Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy.

6. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands.