Meet DataVerse
Rapidly Construct Your AI Models with Data.
Last updated
Rapidly Construct Your AI Models with Data.
Last updated
At Linker VisionAI DataVerse, we're revolutionizing the pace of visual AI model development. We've crafted an intuitive platform to handle vast datasets visually, letting you unlock AI's full potential across a myriad of applications. With us, your innovations will consistently lead the charge.
Whether you're an AI newcomer or a seasoned developer, DataVerse caters to you. Our comprehensive suite seamlessly integrates data management, advanced visualization, search functionalities, trend analysis, robust annotation tools, and automated AI model training and predictions. Simplified, streamlined, unified—DataVerse is your end-to-end solution for AI development.
Why DataVerse?
Tailor-make your AI experience with flexible ontologies, guiding you in data and model personalization.
Dive into visual tools, ideal for data handling, searching, and merging.
Collaborate effortlessly. Our platform bridges data curation to AI model execution, making teamwork both efficient and effective.
👉 Get Started with Linker VisionAI DataVerse!
Got 2 minutes? Check out a video overview of VisionAI DataVerse (template):
Follow our handy guides to get started on the basics as quickly as possible:
Before using all the features of the platform, it is recommended to set up your project information in advance, as this can help you quickly configure your data and model objectives in the future.
Starting with a bunch of images or videos? Try folder upload. And if you're starting from scratch, pop in prepared open datasets and get rolling.
Good to know: Establish a project and set up ontology before beginning any data operations.
Learn the fundamentals of DataVerse to get a deeper understanding of our main features:
Once the data is collected, the next step is data management, visualization, and filtering. This stage involves:
Visualizing the data to gain insights into patterns and trends.
Organizing the data into data slices based on specific criteria to create subsets for training, validation, and testing purposes.
Discovering relevant images within your data is now as easy as typing a sentence.
Good to know: Data Discovery makes the most of your search by using specific keywords. They're like magic spells to unveil hidden treasures.
With the data organized and filtered, you can now use the labeling tool to efficiently annotate your data slices.
After annotating the data, the next step is to train your AI model. This involves:
Selecting an appropriate model architecture based on the application domain.
Training the model using the training data, while monitoring performance on the validation set.
Fine-tuning the model's parameters and hyperparameters to optimize performance.
Evaluate its performance using the test set. This involves calculating various performance metrics, such as precision, recall, and F1 score, to assess the model's effectiveness in solving the problem at hand.
Good to know: Begin with simpler models – they're like your warm-up laps. Once you're in the groove, you can amp up the intensity for better performance. And remember, good training takes time. So, grab a coffee and let the platform work its magic.
With a trained and evaluated AI model, the final step is deployment. This involves integrating the model into your application or system, making it accessible to users or other systems for real-world use.
The system will pre-create a bbox data sample Project. You can try various functions through Get Strtaed > Go to Demo Project first.
