Human-verified annotation and model output validation.
Data Labelling & Annotation
Your TEAM and ours. Integrated.
GraphicWeave has extensive experience providing affordable data labelling tailored to different industries.
GraphicWeave collaborates to deploy AI and Machine Learning in Oil and Gas sector, Geospatial Technology, Medical AI, and other industries by enriching, annotating and labeling data.
By partnering with Graphicweave, you can gain access to the resources and expertise you need to thrive in today's competitive business landscape.
Dedicated Team Model
In this model, we will assemble a dedicated team of labelers and project managers who work exclusively on the your labeling tasks. This model is ideal for long-term projects where maintaining consistency and deep knowledge of the labeling guidelines is essential.
On-Demand Labeling Model
This flexible model allows you to scale up or down based on project demands. Rather than committing to a fixed team, you can request labeling resources as needed, allowing for quick adjustments in response to data volume or workflow changes.
The on-demand model is ideal for short-term projects or companies with unpredictable data labeling needs. It also allows for cost savings, as clients only pay for the resources used, making it a budget-friendly choice for fluctuating or smaller workloads.
The dedicated team becomes highly familiar with the specific requirements, providing greater quality and efficiency over time. This model works well for projects with complex or domain-specific labeling needs, such as medical or legal data annotation.
Types of Data Labelling we do
COMPREhensive set of services
Image Classification
Labeling whole images according to their primary content or purpose. This might involve tagging images as “cat,” “dog,” or “car,” enabling a model to recognize specific objects in an image.
Object Detection
Annotating images with bounding boxes around multiple objects within a scene. Each object is tagged with a label, helping models learn to identify and locate specific items, such as vehicles or pedestrians, in a picture.
Pose Estimation
Labeling key points on a human body or other objects within an image or video to track movement and positions. It’s useful in areas like sports analytics, animation, and healthcare.
Semantic Segmentation
Labeling each pixel within an image with a specific class label. This creates a detailed mask that allows the model to understand the exact shape and boundary of objects, useful in applications like autonomous driving and medical imaging.
Instance Segmentation
Similar to semantic segmentation, but with a unique label for each instance of an object in the image. This approach is used when it’s essential to distinguish between different instances of the same object, like identifying and separating multiple people in a crowd.
Bounding Box Annotation
Drawing boxes around objects within an image to indicate their locations. This simple yet powerful technique allows a model to recognize and detect specific objects, such as cars, faces, or animals.
Text Classification
Categorizing text into predefined classes, such as positive or negative sentiment, spam or not spam. It’s commonly used in sentiment analysis, document categorization, and spam filtering.
Named Entity Recognition (NER)
Labeling text with specific entities, such as names of people, locations, organizations, dates, and monetary values. This is essential for natural language processing (NLP) tasks like information retrieval and text mining.
Polyline and Polygon Annotation
Drawing lines or shapes (polygons) to outline irregularly shaped objects, like roads or buildings on satellite images, or defining boundaries in medical images. This is valuable for precise object detection in non-standard shapes.
Audio Labeling
Tagging audio clips with labels such as speech, music, animal sounds, or identifying the spoken language. It enables applications like voice recognition and music recommendation systems to distinguish different types of sounds.
Transcription
Converting spoken language in audio or video files into written text. Transcription is widely used for speech recognition tasks, helping voice-activated systems and virtual assistants understand human speech.
Time-Series Labeling
Labeling data points over time, such as financial market trends, temperature patterns, or equipment monitoring data. This type of labeling is crucial for predictive models that analyze temporal patterns.
Happy clients globally
We have happy clients from Canada, US, UK, Malaysia, Japan, Azerbaijan, Jordon, UAE, Australia and locally.
