Vectorizer.ai

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Vectorizer.AI converts JPEGs and PNGs to high-quality SVGs using AI, for free during Beta phase.

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What is Vectorizer.ai?

Vectorizer.AI is an innovative web-based tool that harnesses artificial intelligence to transform pixel-based images (JPEGs and PNGs) into high-quality vector graphics (SVGs). This conversion process is fully automated, requiring no manual adjustments as the AI executes the transformation independently. During its Beta phase, Vectorizer.AI is accessible for free, featuring a user-friendly drag-and-drop interface for image uploads. By utilizing AI-powered vectorization, the tool ensures that the resulting vectors maintain scalability without compromising resolution, making them suitable for a range of applications such as printing, cutting, and embroidery. Additionally, Vectorizer.AI supports various vector and bitmap formats like PDF, EPS, DXF, and PNG, catering to designers, artists, and businesses seeking a versatile graphic tool.

Who created Vectorizer.ai?

Vectorizer.ai was created by Cedar Lake Ventures, Inc., a small company based in the Minneapolis area. The founder, James Diebel, initiated the research project that led to Vector Magic, the company's first website, during his time as a graduate student at Stanford University. Jacob Norda, another key figure, collaborated with Diebel on this project after working in software engineering roles at various companies. Orna Kretchmer, with a background in Biology and Molecular Biology, joined Cedar Lake Ventures in 2013. The team also includes Jonathan Kretchmer, a Music Composition graduate. The company's tool, Vectorizer.AI, leverages AI technology to automatically convert bitmap images into high-quality vector graphics, offering support for various formats and precise outputs.

What is Vectorizer.ai used for?

Converting pixel-based images (JPEGs and PNGs) into high-quality vector graphics (SVGs)
Scaling vector graphics to any size without loss of resolution
Ideal for printing, cutting, embroidering, and other applications
Support for various vector and bitmap formats like PDF, EPS, and DXF
Utilizes AI networks and classical algorithms for efficient image conversion
Full shape fitting beyond simple Bezier curves for precision
Employs GPUs for deep learning and multi-core CPUs for quick processing
Achieves sub-pixel precision for accurate vectorization
Supports output formats including SVG, PDF, EPS, DXF, and PNG
Clean corners, tangent matching, and curve fairing to improve results
Printing
Cutting
Embroidering
Web graphics
High-DPI screens
Laser engraving
Vinyl cutting
Screen printing
Editing in vector editors
Artificial intelligence training data (subject to commercial use limitations)
Converting pixel-based images (JPEGs and PNGs) into high-quality, full-color vector graphics (SVGs)
Ideal for various applications like printing, cutting, and embroidering
Ability to scale vectors to any size without loss of resolution
Support for other vector and bitmap formats including PDF, EPS, DXF, and PNG
Versatile tool for designers, artists, and businesses
Composing geometric shapes that can be easily edited and changed in a vector editor
Commonly used for printed graphics and web graphics
Necessary for some types of printing processes such as laser engraving, vinyl cutting, and screen printing
Ideal for various applications like printing, cutting, embroidering
Support for other vector and bitmap formats, including PDF, EPS, DXF, and PNG
Scalable vectors that can be resized to any size without loss of resolution
Offers support for geometric shapes beyond simple Bezier curves for precision
High performance with GPUs for deep learning and multi-core CPUs for quick processing
Output formats include SVG, PDF, EPS, DXF, and PNG
Useful for printed graphics, web graphics, laser engraving, vinyl cutting, and screen printing
Scaling vector images to any resolution without losing quality, ideal for various applications like printing, cutting, and embroidering
Full shape fitting, supporting various curve types and geometric shapes beyond simple Bezier curves for precision
High-performance processing employing GPUs for deep learning and multi-core CPUs
Sub-pixel precision for achieving details finer than one pixel in vectorization
Automatically converting bitmap images into vector images that can be scaled without blurriness
Clean-up and improvement of AI vectorizer output through fitting geometric shapes, cleaning up corners, tangent matching, and curve fairing
Ongoing development with exciting features in the pipeline
Scaling resulting vectors to any size without loss of resolution
Ideal for various applications like printing, cutting, embroidering, and more
Equipped with a Deep Vector Engine utilizing AI networks and classical algorithms for efficient image conversion
Supports full shape fitting, offering support for geometric shapes beyond simple Bezier curves for precision
Provides high performance by employing GPUs for deep learning and multi-core CPUs for quick processing
Achieves sub-pixel precision for details finer than one pixel in vectorization
Support for various output formats like SVG, PDF, EPS, DXF, and PNG
Capability to work on AI-generated images and deliver impressive results
Ideal for applications like printing, cutting, embroidering, etc.
Deep Vector Engine for efficient image conversion
Full Shape Fitting for precision with geometric shapes
High Performance using GPUs and multi-core CPUs
Sub-Pixel Precision for accuracy in vectorization
Supports various export formats like SVG, PDF, EPS, DXF, and PNG
Automatically converts bitmap images into vector images that can be scaled without loss of quality
AI-powered vectorization process ensures high-quality results

Who is Vectorizer.ai for?

Designers
Artists
Businesses

How to use Vectorizer.ai?

To utilize Vectorizer.ai effectively, follow these steps for seamless navigation of the tool:

Access the Vectorizer.ai platform via your web browser.
Sign up for an account using your email or preferred social media login.
Familiarize yourself with the dashboard layout and available features.
Begin by creating a new project or selecting an existing one to work on.
Upload your dataset or input the necessary data directly into the tool.
Choose the type of analysis you wish to perform, such as classification or clustering.
Configure the parameters for the analysis, including the target variable and any specific settings.
Initiate the analysis process and allow the tool to generate insights based on your data.
Explore the results provided by Vectorizer.ai, including visualizations and statistical summaries.
Refine your analysis by adjusting parameters or exploring different models.
Save your progress periodically to ensure data preservation and easy access for future reference.
Download the final results, reports, or visualizations for reporting or further analysis outside the platform.

By following these steps, users can make the most of Vectorizer.ai's capabilities for data analysis and insights generation.

Pros

High Performance: Employs GPUs for deep learning and multi-core CPUs for quick processing.
Supports JPEG, PNG, WEBP, BMP, and GIF as input, and SVG, PDF, EPS, DXF, and PNG as output.
Full transparency support with 32-bit ARGB input.
Ideal for printing graphics, web graphics, laser engraving, vinyl cutting, and screen printing.
Easily edited and changed in a vector editor.
Provides a free, interactive preview to see results before purchase.
Utilizes AI networks and classical algorithms for efficient image conversion
Offers support for geometric shapes beyond simple Bezier curves for precision
Employs GPUs for deep learning and multi-core CPUs for quick processing
Achieves details finer than one pixel for accuracy in vectorization
Supports various formats like SVG PDF EPS DXF and PNG for output
Deep Vector Engine: Utilizes AI networks and classical algorithms for efficient image conversion.
Full Shape Fitting: Offers support for geometric shapes beyond simple Bezier curves for precision.
New approach to vectorization with promising results.
Sub-Pixel Precision: Achieves details finer than one pixel for accuracy in vectorization.

Cons

Not suitable for training machine learning models
No customization options available for the vectorization process
Limited input file size (max image file size is 30 megabytes)
No support for machine learning training usage of the output
Limited scope of supported file formats compared to some competitors
No automatic renewal of credits when running low
Limited to input images with a max size of 3 megapixels and 30 MB file size
Limited output formats (SVG, PDF, EPS, DXF, PNG)
Lack of configuration options for the AI
No special pricing for high volume API calls
Limited data retention policies (images and results retained for 24 hours)
No support for machine learning model training with the output
Access is granted immediately when subscribing, not offering a trial period for new users
Limited to 3-megapixel image pixel size and 30 MB file size
Limited file format support for input and output

Pros

Cons

High Performance: Employs GPUs for deep learning and multi-core CPUs for quick processing.
Supports JPEG, PNG, WEBP, BMP, and GIF as input, and SVG, PDF, EPS, DXF, and PNG as output.
Full transparency support with 32-bit ARGB input.
Ideal for printing graphics, web graphics, laser engraving, vinyl cutting, and screen printing.
Easily edited and changed in a vector editor.
Provides a free, interactive preview to see results before purchase.
Utilizes AI networks and classical algorithms for efficient image conversion
Offers support for geometric shapes beyond simple Bezier curves for precision
Employs GPUs for deep learning and multi-core CPUs for quick processing
Achieves details finer than one pixel for accuracy in vectorization
Supports various formats like SVG PDF EPS DXF and PNG for output
Deep Vector Engine: Utilizes AI networks and classical algorithms for efficient image conversion.
Full Shape Fitting: Offers support for geometric shapes beyond simple Bezier curves for precision.
New approach to vectorization with promising results.
Sub-Pixel Precision: Achieves details finer than one pixel for accuracy in vectorization.

Not suitable for training machine learning models
No customization options available for the vectorization process
Limited input file size (max image file size is 30 megabytes)
No support for machine learning training usage of the output
Limited scope of supported file formats compared to some competitors
No automatic renewal of credits when running low
Limited to input images with a max size of 3 megapixels and 30 MB file size
Limited output formats (SVG, PDF, EPS, DXF, PNG)
Lack of configuration options for the AI
No special pricing for high volume API calls
Limited data retention policies (images and results retained for 24 hours)
No support for machine learning model training with the output
Access is granted immediately when subscribing, not offering a trial period for new users
Limited to 3-megapixel image pixel size and 30 MB file size
Limited file format support for input and output

Vectorizer.ai FAQs

What does Vectorizer.AI do?: Vectorizer.AI uses proprietary deep learning networks and classical algorithms to automatically convert bitmap images into vector images such as SVGs, which can then be scaled without losing resolution.

How do I use Vectorizer.AI to convert an image to a vector?: To vectorize an image, drag and drop your bitmap image onto the Vectorizer.AI page. The AI will process it into geometric shapes, and you can then preview and download the vector image in your desired format.

What are some key features of Vectorizer.AI?: Vectorizer.AI features a Deep Vector Engine, various curve types support, full shape fitting, clean corners, sub-pixel precision, and high performance using GPUs and CPUs.

What output formats does Vectorizer.AI support?: Vectorizer.AI supports output formats such as SVG, PDF, EPS, DXF, and PNG, with SVG being the default format.