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.

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.

which is a new state-of-the-art result.

This dataset contains synthetic and real images, with their labels, for Computer Vision in robotic surgery. It is part of ongoing research on sim-to-real applications in surgical robotics. The dataset will be updated with further details and references once the related work is published. For further information see the repository on GitHub: https://github.com/PietroLeoncini/Surgical-Synthetic-Data-Generation-and-Segmentation

The dataset contains 3D point cloud data of a synthetic plant with 10 sequences. Each sequence contains 0-19 days data at every growth stage of the specific sequence.

Geo Fossils-I is a synthetic dataset of fossil images that can be a pioneer in solving the limited availability of Image Classification and Object Detection on 2D images from geological outcrops. The dataset consists of six different fossil types found in geological outcrops, with 200 images per class, for a total of 1200 fossil images.

SynthAer is a dataset consisting of synthetic aerial images with pixel-level semantic annotations from a suburban scene generated using the 3D modelling tool Blender. SynthAer contains three time-of-day variations for each image - one for lighting conditions at dawn, one for midday, and one for dusk.

Synthetic MI data (S21 dB) corresponding to coils tracked in the videos.

This data-set is a supplementary material related to the generation of synthetic images of a corridor in the University of Melbourne, Australia from a building information model (BIM). This data-set was generated to check the ability of deep learning algorithms to learn task of indoor localisation from synthetic images, when being tested on real images. =============================================================================The following is the name convention used for the data-sets. The brackets show the number of images in the data-set.REAL DATAReal
---------------------> Real images (949 images)

Gradmag-Real -------> Gradmag of real data (949 images)SYNTHETIC DATASyn-Car
----------------> Cartoonish images (2500 images)

Syn-pho-real ----------> Synthetic photo-realistic images (2500 images)

Syn-pho-real-tex -----> Synthetic photo-realistic textured (2500 images)

Syn-Edge --------------> Edge render images (2500 images)

Gradmag-Syn-Car ---> Gradmag of Cartoonish images (2500 images)=============================================================================Each folder contains the images and their respective groundtruth poses in the following format [ImageName X Y Z w p q r].To generate the synthetic data-set, we define a trajectory in the 3D indoor model. The points in the trajectory serve as the ground truth poses of the synthetic images. The height of the trajectory was kept in the range of 1.5–1.8 m from the floor, which is the usual height of holding a camera in hand. Artificial point light sources were placed to illuminate the corridor (except for Edge render images). The length of the trajectory was approximately 30 m. A virtual camera was moved along the trajectory to render four different sets of synthetic images in Blender*. The intrinsic parameters of the virtual camera were kept identical to the real camera (VGA resolution, focal length of 3.5 mm, no distortion modeled). We have rendered images along the trajectory at 0.05 m interval and ± 10° tilt.The main difference between the cartoonish (Syn-car) and photo-realistic images (Syn-pho-real) is the model of rendering. Photo-realistic rendering is a physics-based model that traces the path of light rays in the scene, which is similar to the real world, whereas the cartoonish rendering roughly traces the path of light rays. The photorealistic textured images (Syn-pho-real-tex) were rendered by adding repeating synthetic textures to the 3D indoor model, such as the textures of brick, carpet and wooden ceiling. The realism of the photo-realistic rendering comes at the cost of rendering times. However, the rendering times of the photo-realistic data-sets were considerably reduced with the help of a GPU. Note that the naming convention used for the data-sets (e.g. Cartoonish) is according to Blender terminology.An additional data-set (Gradmag-Syn-car) was derived from the cartoonish images by taking the edge gradient magnitude of the images and suppressing weak edges below a threshold. The edge rendered images (Syn-edge) were generated by rendering only the edges of the 3D indoor model, without taking into account the lighting conditions. This data-set is similar to the Gradmag-Syn-car data-set, however, does not contain the effect of illumination of the scene, such as reflections and shadows.*Blender is an open-source 3D computer graphics software and finds its applications in video games, animated films, simulation and visual art. For more information please visit: http://www.blender.orgPlease cite the papers if you use the data-set:1) Acharya, D., Khoshelham, K., and Winter, S., 2019. BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images. ISPRS Journal of Photogrammetry and Remote Sensing. 150: 245-258.2) Acharya, D., Singha Roy, S., Khoshelham, K. and Winter, S. 2019. Modelling uncertainty of single image indoor localisation using a 3D model and deep learning. In ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, IV-2/W5, pages 247-254.

Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the COCO format. For details see our paper and for visual samples our project page.

Relevant computer vision tasks:

• bounding box detection
• classification
• instance segmentation
• keypoint estimation
• 3D bounding box estimation
• 3D voxel reconstruction
• 3D reconstruction

The dataset is for academic research use only, since it uses resources with restrictive licenses.
For a detailed description of how the resources are used, we refer to our paper and project page.

Licenses of the resources in detail:

• Google Scanned Objects: CC BY 4.0 (for details on which files are used, see the respective meta folder)
• Cardboard Dataset: CC BY 4.0
• Shipping Label Dataset: CC BY-NC 4.0
• Other Labels: See file misc/source_urls.json
• LDR Dataset: License for Non-Commercial Use
• Large Logo Dataset (LLD): Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.

You can use our textureless models (i.e. the obj files) of damaged parcels under CC BY 4.0 (note that this does not apply to the textures).

If you use this resource for scientific research, please consider citing

@inproceedings{naumannParcel3DShapeReconstruction2023,
  author  = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai},
  title   = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month   = {June},
  year   = {2023},
  pages   = {4402-4412}
}

About this dataset

This dataset contains 6,000 example images generated with the process described in Roboflow's How to Create a Synthetic Dataset tutorial.

The images are composed of a background (randomly selected from Google's Open Images dataset) and a number of fruits (from Horea94's Fruit Classification Dataset) superimposed on top with a random orientation, scale, and color transformation. All images are 416x550 to simulate a smartphone aspect ratio.

To generate your own images, follow our tutorial or download the code.

Example: https://blog.roboflow.ai/content/images/2020/04/synthetic-fruit-examples.jpg" alt="Example Image">

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

Description

The more realistic version of the DigiFace1M.
DigiFace1M originally created by the 3D rendering engines. When this dataset is used to train Face Recognition systems there is OOD problem by evaluating the trained models against real dataset like LFW, IJB-B and IJB-C. In our paper we tried to address this problem by post processing the original images.
This type of dataset is generated by simply passing the images from DigiFace1M through methods like CodeFormer and VSAIT.

License

RealDigiFace dataset is published for non-commercial research use only, please check the license agreement.

Reference

If you use this dataset, please cite the following publication:

Rahimi, P., Razeghi, B., & Marcel, S. (2024). Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition. ArXiv, abs/2407.07627. European Conference on Computer Vision Workshop of Synthetic Data in Computer Vision, September 2024

The Synthetic Data Platform market is experiencing robust growth, driven by the increasing need for data privacy, escalating data security concerns, and the rising demand for high-quality training data for AI and machine learning models. The market's expansion is fueled by several key factors: the growing adoption of AI across various industries, the limitations of real-world data availability due to privacy regulations like GDPR and CCPA, and the cost-effectiveness and efficiency of synthetic data generation. We project a market size of approximately $2 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033). This rapid expansion is expected to continue, reaching an estimated market value of over $10 billion by 2033. The market is segmented based on deployment models (cloud, on-premise), data types (image, text, tabular), and industry verticals (healthcare, finance, automotive). Major players are actively investing in research and development, fostering innovation in synthetic data generation techniques and expanding their product offerings to cater to diverse industry needs. Competition is intense, with companies like AI.Reverie, Deep Vision Data, and Synthesis AI leading the charge with innovative solutions. However, several challenges remain, including ensuring the quality and fidelity of synthetic data, addressing the ethical concerns surrounding its use, and the need for standardization across platforms. Despite these challenges, the market is poised for significant growth, driven by the ever-increasing need for large, high-quality datasets to fuel advancements in artificial intelligence and machine learning. The strategic partnerships and acquisitions in the market further accelerate the innovation and adoption of synthetic data platforms. The ability to generate synthetic data tailored to specific business problems, combined with the increasing awareness of data privacy issues, is firmly establishing synthetic data as a key component of the future of data management and AI development.

About Dataset

Overview This dataset contains images of synthetic road scenarios designed for training and testing autonomous vehicle AI systems. Each image simulates common driving conditions, incorporating various elements such as vehicles, pedestrians, and potential obstacles like animals. In this specific dataset, certain elements, such as the dog shown in the image, are synthetically generated to test the ability of machine learning models to detect unexpected road hazards. This dataset is ideal for projects involving computer vision, object detection, and autonomous driving simulations.

To learn more about how synthetic data is shaping the future of AI and autonomous driving, check out our latest blog posts at NeuroBot Blog for insights and case studies. https://www.neurobot.co/use-cases-posts/autonomous-driving-challenge

Want to see more synthetic data in action? Head over to www.neurobot.co to schedule a demo or sign up to upload your own images and generate custom synthetic data tailored to your projects.

Note Important Disclaimer: This dataset has not been part of any official research study or peer-reviewed article reviewed by autonomous driving authorities or safety experts. It is recommended for educational purposes only. The synthetic elements included in the images are not based on real-world data and should not be used in production-level autonomous vehicle systems without proper review by experts in the field of AI safety and autonomous vehicle regulations. Ensure you use this dataset responsibly, considering ethical implications.

SYNTHETIC dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022.

In order to fully reproduce the experiments, download also the REAL dataset.

To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below.

Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments

A dataset consisting of Ca II & H-alpha images taken at the Paris Meudon Observatory. Synthetically generated cloud coverage has been applied to clean images, thereby creating an (cloudy, clean) pair--facilitating the training of cloud-removal algorithms.

Data description

The Ca-II and H-α synthetic dataset comprise respectively 319 and 367 pairs of shadow/shadow-free images, split into 223/96 and 256/111 training/testing pairs.

Listed here are two zip archives:

1. filament-bounding-boxes.zip -- bounding boxes of filaments that were used to compute the patched metrics.
2. synthetic-clouds.zip -- the cloudy input/clean output images that are used to train machine learning algorithms.

A PyTorch dataset has been created that handles the download, importing, and usage of this dataset. You can find this code at the github repository: https://github.com/jaypmorgan/cloud-removal

Pre-processing routines

To generate this set of data, we have applied a series of pre-processing routines. These are:

1. Correct determination of the solar limb (source code can be found at: https://gitlab.lis-lab.fr/presage/solar-limb-detection).
2. Scaling the solar disk to 420 pixels, and centring it at 511.5 pixels in the x and y dimensions.
3. Setting background values outside the solar disk to 0.
4. Normalising the disk intensity values into the range of 0-1.

This dataset contains synthetic, labeled medical images (such as X-rays and MRIs) with detailed annotations for use in computer vision model training and diagnostic tool development. Each record includes image metadata, patient demographics, annotation details (including spatial data and confidence scores), and diagnostic labels, making it ideal for research, benchmarking, and algorithm validation in medical imaging.

The AI computer vision market is experiencing explosive growth, driven by increasing adoption across diverse sectors. While precise market sizing requires proprietary data, a logical estimation based on typical CAGR for rapidly evolving technology sectors (let's assume a conservative 20% CAGR for illustrative purposes) suggests a market size exceeding $50 billion by 2025, expanding to well over $200 billion by 2033. Key drivers include the proliferation of connected devices generating vast amounts of visual data, advancements in deep learning algorithms enhancing accuracy and efficiency, and the falling cost of computing power enabling wider accessibility. The automotive, healthcare, retail, and security industries are major adopters, leveraging computer vision for tasks like autonomous driving, medical image analysis, inventory management, and surveillance. Emerging trends include the integration of edge computing to reduce latency and improve real-time processing capabilities, the rise of synthetic data for training AI models, and growing interest in explainable AI to enhance transparency and trust in computer vision applications. However, challenges remain, including concerns over data privacy and security, the need for robust data annotation processes, and the ongoing development of algorithms capable of handling complex visual scenarios and diverse lighting conditions. Significant market segmentation exists, categorized by application (automotive, healthcare, retail, etc.), technology (deep learning, convolutional neural networks, etc.), and deployment model (cloud, on-premises, edge). The competitive landscape is highly fragmented, with established players like NetApp, IBM, SAS, and Amazon vying for market share alongside innovative startups and specialized AI companies. The market's success hinges on continued innovation in algorithm development, expanding data availability, and addressing ethical concerns related to bias and misuse. The forecast period (2025-2033) promises substantial growth, but success will depend on companies' ability to overcome existing limitations and capitalize on emerging opportunities.