Comprehensive Tools for Business and Individual Customers

Skinner AI presents an innovative distributed deep-learning training framework with a focus on Computer vision applications for drones, robotics, and autonomous vehicles. In addition to its distributed training capabilities, Skinner features a unique Synthetic data generator that allows users to create, buy, and sell high-quality datasets derived from Photogrammetric, Generative or custom 3D models. Using these technologies, Skinner ensures the integrity and reliability of its training processes and solves the important problem of retraining neural networks. That should have multiple global implications, including advances in health, education, climate science, etc.

Synthetic Data

A key feature of the Skinner AI framework is its Synthetic data generator. This innovative tool allows users to easily create, buy, and sell high-quality datasets derived from Photogrammetric, Generative or custom 3D models, unlocking vast potential for machine learning training.

Operating as a cloud computing solution, it offers scalable, on-demand data generation capabilities suitable for a broad range of applications.

Synthetic Data Generator: This feature allows users to create, buy, and sell high-quality datasets derived from 3D models. This functionality is particularly valuable for training and testing computer vision models for drones, robotics, and autonomous vehicles, where real-world data can be challenging to gather.

Customization

Customization of Synthetic data generator refers to the process of tailoring or modifying a data generation tool to create Synthetic datasets that meet specific requirements or mimic real-world scenarios. Synthetic data generators are software tools and algorithms that produce artificial datasets with characteristics similar to real data.

By customizing these generators, researchers, developers, or data scientists can create datasets that are suited for their particular needs.

Through customization, Synthetic data generators can be fine-tuned to generate datasets that closely match specific use cases, industries, or research domains. This flexibility allows for the creation of diverse datasets with different complexities, enabling users to validate and evaluate their algorithms, train machine learning models, or conduct experiments in a controlled and privacy-preserving environment.

Decentralization

Cloud computing's inherent decentralization ensures that no single entity controls the Skinner network. This is essential in a distributed computing environment where multiple parties contribute resources for machine learning training. Decentralization enhances the resilience of the system, prevents monopoly of resources, and ensures fair use and access.

Decentralized technology provides a clear and indisputable record of data ownership and provenance. As users create, buy, and sell datasets on the Skinner AI platform, the blockchain keeps track of these transactions, providing a clear trail of data ownership. This is crucial for data licensing, copyright issues, and ensuring fair compensation for dataset creators.

In conclusion, cloud computing technology is not just an add-on, but a component of its secure, and transparent nature making it the ideal backbone for Skinner AI's distributed deep learning training and Synthetic data generation platform.

Generation Tools

The Synthetic data generator tools offer a range of features and options to control the generation process. Users can adjust lighting conditions, textures, and material properties to create different variations of the same scene. They can also introduce different environmental factors, such as weather conditions or physical interactions, to simulate real-world scenarios.

One of the main advantages of these tools is the ability to generate large volumes of labeled and annotated Synthetic data, which can be used for training and testing machine learning models. By providing a diverse dataset with known ground truth, these tools help improve the accuracy and robustness of AI systems, particularly in domains like computer vision, robotics, and autonomous driving.

In summary, the Tools of Synthetic data generator from 3D models empower researchers, developers, and data scientists to create realistic and diverse datasets for training and testing purposes. By leveraging the capabilities of models and simulation techniques, these tools enable the generation of Synthetic data that closely resembles real-world scenarios, fostering advancements in various fields of artificial intelligence.

Software Upgrades

Software upgrades for Synthetic data generators aim to enhance the capabilities and efficiency of these tools used in data generation processes. These upgrades often incorporate advanced algorithms and features to produce high-quality, realistic Synthetic data sets.

Aspect of software upgrades involves improving the diversity and variability of generated data. By implementing sophisticated algorithms, the software can generate Synthetic data that closely mimics the statistical properties and patterns observed in real-world data. This ensures that the generated data accurately represents the target population and supports reliable analysis and testing.

Overall, software upgrades for Synthetic data generators strive to enhance the quality, versatility, and efficiency of the generated data. They empower users with more advanced customization options, improved performance, and robust privacy measures, ultimately facilitating more accurate and effective data-driven analyses, machine learning model training, and testing.

Automatic Labeling

By automating the labeling process, this generator significantly reduces the time, effort, and costs associated with manual data labeling. It enables researchers, data scientists, and developers to rapidly generate large volumes of labeled Synthetic data, which can be used for various purposes, including training and evaluating machine learning models, testing algorithms, and enhancing data privacy.

The automatic labeling of Synthetic data generator ensures that the labels assigned to the Synthetic data are realistic and align with the desired properties of the target data set. This helps in creating more accurate and representative training data, ultimately improving the performance and robustness of AI models across different domains and applications.

Overall, this technology serves as a valuable tool in the data generation pipeline, accelerating the development and deployment of AI systems by providing an efficient and reliable solution for generating labeled Synthetic data.

Remote Support

With remote support, users can receive guidance, advice, and assistance with various aspects of using synthetic data generator tools without the need for an in-person presence. Remote support can be provided through various channels, such as phone calls or video conferencing, allowing experts to remotely diagnose and resolve any issues users may encounter.

Remote support for synthetic data generator tools can cover a wide range of activities, including installation and setup assistance, configuration guidance, troubleshooting errors or bugs, optimizing performance, and providing recommendations for best practices.

Additionally, remote support can help users understand the features and functionalities of the software, ensuring they can effectively utilize the tool to generate high-quality synthetic datasets.

Skinner's rates

Individual Tools

for Developers
$ 20
  • Pay-Per-Input License/Equals to 2,000 data generations

Business Tools

For Corporate
$ Custom
  • On-Premise License​/Personal Account Manager​​/Offline use

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