· Solve problems with human input
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Image Annotation
Bounding Boxes
Drawing 2-dimensional frames around objects of interest (cars, pedestrians, trees) allows a machine to classify these objects into predefined categories.
Polygons
2D boxes may be insufficient for machine learning training as understanding object shape can be vital. Using polygons to outline objects of varying sides enables machines to identify objects by shape.
Semantic Segmentation
Every image pixel is linked to an object class (e.g., car, person, etc.). Machine learning models group same-class pixels, creating a map of various object clusters for model training.
OCR
OCR transforms images of text into regular text, instructing machine learning algorithms to convert scanned documents or photocopies into machine-encoded text.
Video Annotation
Bounding Boxes
It is the most used type of video annotation. We use rectangular box annotation to illustrate objects and create training data so algorithms can identify and localize objects during ML processes.
Polygons
Expert annotators plot points on each vertex of the target object. Polygon annotation allows all of the object’s exact edges to be annotated, regardless of shape.
Semantic Segmentation
Videos are segmented into component parts, by us, and then annotated. We examine video frames and classify objects pixel by pixel.
Key Points
Identify and mark key points of an object in videos, such as eyes, noses, lips, or even individual cells.
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Data Management
Data Curation
We extract and organize relevant material from vast sets of unstructured text and visual data to enable the automation of manual processes and streamlining of operations.
Data Collection
If you require a dataset but lack the time or resources, provide us with your needs. We'll deliver high-quality, relevant, and consistent data for seamless AI model training.
Data Entry
Our team carries out the process on spreadsheets, handwritten or scanned documents, audio files, or videos. With data entry services, we aim to help our clients conduct analyses and develop business strategies.
Data Validation
It is essential for accurate ML training, ensures data accuracy, consistency, and relevance. It applies techniques to rectify errors, prevent overfitting or underfitting, and yield more precise results.
NLP Annotation
Text Classification
One of the major tasks of NLP, text classification involves dividing text into groups based on their content. Topic labeling and spam detection are good examples of text classification.
Named Entity Recognition
It identifies and categorizes entities (specific words or phrases) within text, aiding machine learning models in summarizing and traversing large text volumes.
Intent/Sentiment Analysis
Sentiment analysis categorizes texts based on tone, benefiting market research, brand reputation, and customer experience understanding. Intent analysis discerns the text's purpose, such as identifying phishing emails.
Comparison
Certain machine learning tasks need to compare one text to a different text (or texts) and identify how similar they are. Comparison is used to find semantically similar texts.
Benefits
Data labeling, performed by a globally distributed user base
Our data annotation services are powered by 2.5 million annotators from 43 countries. With our global audience, you can take advantage of the 11 different languages we support.
Quality assurance, done by our system
Our created algorithm validates human-labeled data with high precision using adjustable consensus levels, catering to your machine learning data's specific accuracy needs.
Different annotation support
Recognizing that your AI training data is unique, we are eager and adaptable, prepared to annotate any data type, even those previously unexplored!
Cost effective
Our pricing is transparent and straightforward. We are cheaper than AWS Sagemaker or other platforms. With adjustable consensus and accuracy settings, our systems deliver optimal results without repetitive tasks.
Guaranteed completion time
Leveraging our 2M workforce's diverse time zones and language skills, we can promptly launch and deliver results for machine learning projects. Average turnaround time is 24 hours.
Data security
Data security is of paramount importance. We are GDPR and CCPA compliant.
QUALITY ASSURANCE
Quality is our main goal.
Annotation tasks submitted to the platform are manually done and reviewed by our human audience. The resulting object detection and image classification accuracy are consistently higher than what automatic data labeling approaches can achieve.
2x
Min. human consensus
2.5M
Annotator base
"AttaLab.ai came out to be our main source of image labeling and classification, thanks to the highly accurate AI technology, the speed of project completion, and simplicity of the setup process. I highly recommend AttaLab.ai to anyone in need of reliable and accurate data analysis."
Stasys Savilionis
Data Manager, JSC Vilniaus Planas
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