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I will do data annotation for ai
Virtual Assistant, Advanced web researcher, Data entry expert
Hi, I am Azizul. I am very interested to work as a virtual assistant. My working sites are data collection, data conversion, data mining, lead generation, B2B, copy paste work, PDF, excel, power point...
About this Gig
⭐ Professional Data Annotation & Labeling Services ⭐
Hello!
I am a skilled Data Annotator with hands-on experience in image, text, and audio annotation for AI and Machine Learning projects. I help businesses, startups, and researchers build high-quality labeled datasets that improve model accuracy and performance.
Services I Offer:
Image Annotation (Bounding Box, Polygon, Semantic Segmentation, Key-points)
Video Annotation & Tracking
Text Annotation (NER, sentiment analysis, intent labeling)
Audio Annotation (Speech labeling, transcription, time-stamping)
Data Categorization & Tagging
OCR & Document Annotation
Why Choose Me?
High accuracy & attention to detail
Manual and tool-based annotation
Fast delivery & clear communication
100% data confidentiality
Quality check before final delivery
Tools & Formats:
CVAT, Labelbox, Label Studio, SuperAnnotate, LabelMe, and VGG Image Annotator (VIA)
The "output" refers to the format in which these annotations are saved, typically JSON or XML files
I am committed to delivering clean, consistent, and reliable annotations tailored to your project needs. Feel free to message me before ordering to discuss your dataset and requirements.
Lets build
Technique:
Manual
Tagging type:
Text
•
Image
•
Audio
My Portfolio
FAQ
What is image annotation and why is it important for my business?
Image annotation is the process of labeling or classifying images to highlight specific features, creating training data for machine learning and deep learning models. It is crucial for developing accurate AI systems, such as object detection for autonomous vehicles or product categorization.
What types of image annotation are available?
Common types include bounding boxes, polygonal segmentation, key points, and semantic segmentation. The appropriate method depends on the specific use case and the required level of detail.
Can you handle domain-specific projects (e.g., medical, agriculture)?
Yes, many providers have teams with specialized domain knowledge required for complex, industry-specific annotations. Buyers should verify the vendor's experience in their particular field.
Do you use manual, automated, or AI-assisted annotation?
Most high-quality services use a combination. Manual annotation ensures accuracy, while AI-assisted tools improve efficiency and speed, which can affect QA and rework.
What happens if the quality standards are not met?
Reputable providers have grievance processes and correction workflows. Errors are typically flagged, corrected by the annotator, and re-verified by a QA specialist to ensure all issues are resolved.
How do you manage large volumes of data within tight deadlines?
This involves effective time management, a scalable workforce, and efficient workflow systems to maintain both speed and accuracy.
