Professional Image Annotation Services
We offer accurate image annotation services to help companies improve their AI models by precisely labelling and tagging images for enhanced computer vision functionality.
Precision Image Annotation Solutions
Image annotation is the act of applying relevant labels or tags to images that allow artificial intelligence (AI) and machine learning (ML) models to learn and generate predictions based on visual data. The service is essential in the training of computer vision powered models to identify objects, actions, and patterns in images. Annotating images in different ways, e.g. tagging labels, bounding boxes, and/or key-points, gives the business assurance that the AI models gain the adequate context for understanding in a real-world application.
Image annotation has lasting advantages for companies likely to enhance their AI capabilities. Image annotation improves an AI computer vision model by creating useful, high quality annotated datasets that are critical to building your models. Accurate image annotation leads to more reliable and accurate AI predictions, which improves automation, increases operational efficiencies, and accelerates decisions.
Image annotation services also help train models to identify more complex patterns across industries successfully, including healthcare, automotive, security, and retail.
Statswork offers professional image annotation services that are customized to fit your needs. Each project is handled by our team of professionals, providing a reliable consistent and precise annotation – no matter the annotation project type, whether object detection, image segmentation or even facial recognition. Our image annotation techniques and tools provide an industry-leading annotate data at speed to speed up your AI model training so that your AI applications reach the accuracy and performance you want.
We offer different types of image annotation to ensure that we can be flexible in accommodating diverse needs for your AI projects. Our image annotation services cover a variety of machine learning projects from object detection to segmentation.
2D Bounding Box Annotation
We offer 2D bounding box annotation for detecting and labelling objects in pictures using bounding boxes. This is a standard technique in object detection, where AI models and algorithms take advantage of the provided annotations to detect and localize objects in pictures.
Polygon Annotation
We provide efficiencies that help keep the data reliable and uniform across your systems, so you can reduce errors and have comfort in your data.
Semantic Segmentation
We offer semantic segmentation, which segments images into distinguishable areas and labels each respective section with a category. Semantic segmentation is very applicable for autonomous driving applications and requires understanding complex images of people and roads (e.g., roads, pedestrians, cars, etc).
Landmark & Key Point Annotation
We provide landmark and key point annotation, which annotates distinct points on objects (e.g., facial landmarks or human joint points). This is very important for applications such as facial recognition, pose estimation and emotion estimation.
Medical Image Annotation
We provide medical image annotation (identifying tumours, lesions, or organs in medical images, e.g. CT scans, MRIs, or X-rays.) These annotations are critical for training AI models for the medical diagnostic field to enhance their accuracy and effectiveness in the healthcare sector.
Industries
Data collection allows sectors to train computer vision models, improve automation, improve diagnostics, ensure safety, and spur innovation via AI applications.
Image annotation is the process of labelling images to train AI models. The image annotation process involves gathering images, applying annotations, either in the form of labels or shapes like bounding box or polygons, and reviewing the annotation for accuracy, and then exporting the annotation to create a training dataset for an AI model.
Input: Unprocessed images from different sources (cameras, drones, satellites, etc.).
Output: Annotated data with labels, bounding boxes, key points, or segmentation masks for training an AI model to recognize patterns.
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