Annotating Data For The Next Generation of Computer Vision Models
Computer vision (CV) is a pioneering technology that equips machines with an understanding of visual data.
CV-enabled technologies are everywhere, from smartphone apps to driverless vehicles and IoT devices.
Aya Data's skilled annotation team can deliver high-quality image data annotation services for a range of applications.
Computer Vision Annotation Services
Bounding Boxes: Optimize 2D Object Tracking With Bounding Box Annotation
Bounding boxes are a well-established labeling and annotation technique for computer vision (CV) projects.
Bounding box labels prepare data for all manner of object detection tasks, including self-driving vehicles, image labeling for visual search, automated insurance claims checking, and medical imaging, to name but a few.
Our in-house team ensures pixel-perfect tightness, consistent variation in box size, and reduced overlap between boxes, maximizing the quality of our datasets.
Bounding box object detection tasks range considerably in size and scope - Aya Data will work with you to establish your unique project’s needs before mobilizing our team of professional labelers and data practitioners.
Even for seemingly simple projects, the quality of your image training and testing datasets relies on the quality of your labels, and every label matters.
Polygon Annotation: Increase AI Accuracy Through Precision Object Identification
Polygon annotation uses multiple coordinates to effectively map the shape of complex shapes. Depending on the purpose of the dataset, polygon annotation will significantly enhance the accuracy of a CV model when dealing with real-world irregular and dynamic shapes.
Aya Data understands when a project demands polygon annotation services as opposed to bounding box labeling or when one will complement or augment the other. We leverage the very best polygon labeling tools to ensure pinpoint accuracy.
We can capture the detail of both simple and complex high-definition polygon images or videos using anything from a small number of vertices to many thousands of vertices.
Image Segmentation: Categorize Image Elements, Pixel By Pixel
Image segmentation divides complex images and videos into multiple segments at pixel level.
Combining semantic segmentation, instance segmentation, and panoptic segmentation makes it possible to identify multiple images by instance or class, allowing models to discern which pixels belong to which class across separate instances.
Our semantic image segmentation services leverage modern labeling tools to deliver reliable, accurate, and detailed results.
Aya Data’s experienced team of data experts has proven experience in creating highly accurate datasets, even in the most complex use cases.
Key Point Annotation
Similar to polygon annotation, landmarking (also known as keypoint or point annotation) involves using a small collection of connected points to annotate multiple objects with similar shapes.
This technique is especially useful for complex objects that can’t be accurately labeled via bounding boxes or polygons.
Human body parts, including facial features, are a prime example of where landmarking is an effective labeling technique. For instance, in facial keypoint detection, landmarks can be placed on critical facial features, such as the eyes, nose, mouth, and jawline.
This enables the localization of specific features within the object.
What’s a Rich Text element?
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
Static and dynamic content editing
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
How to customize formatting for each rich text
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
- text for bullet points