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Precise. Efficient. Dependable. I focus on delivering high-quality annotated data for AI and machine learning projects, with solid experience handling image and video labeling tasks. I’ve worked on various datasets that require careful attention to detail, ensuring every annotation is accurate, consistent, and aligned with project standards. My core strengths include: • Image & Video Annotation • Semantic Segmentation & Masking • 2D and 3D Annotation (Remotasks) • Bounding Boxes, Polygons, and Keypoints • Dataset Organization & Preparation • Quality Assurance and Error Checking I take accuracy seriously, small details matter, and I make sure every output meets the required guidelines. ✨ Tools I’ve worked with: • CVAT • Roboflow • SuperAnnotate • Supervisely • Labelbox • LabelImg • LabelMe In addition to annotation, I also provide support in: 📊 Data & Admin Support • Data Entry and Data Cleanup • Content Review / Moderation • QA and Application Testing • Online Research and General VA Tasks 🎬 Creative Background (Added Advantage) With experience in video editing and design, I have a strong eye for visuals, which is useful when working with media-based datasets: • Adobe Premiere Pro • After Effects • Photoshop & Illustrator • CapCut, Canva, Filmora This combination of technical annotation skills + visual understanding allows me to work effectively on projects involving images, videos, and creative assets. I’m adaptable, quick to learn new systems, and easy to collaborate with. If you’re looking for someone who can deliver accurate annotations and dependable support, I’m ready to contribute to your project.

I am a detail-oriented Data Annotator with experience in labeling and organizing data for machine learning and AI projects. I have skills in bounding box annotation, segmentation, object labeling, and text classification. I also perform data cleaning and quality checks to ensure accuracy and consistency. Services: - Image Classification - Keypoint Annotation - Semantic/Instance Segmentation (Masks, Polygons) - Object Detection (Bounding Boxes) - Object Recognition (Polygons) Tools I Use: - Roboflow - SuperAnnotate - CVAT - LABELBOX

Your model is only as smart as the person labeling its data! Bad training data is the #1 reason ML projects miss the accuracy targets. Mislabeled frames and skipped edge cases compound into models that fail on real-world inputs. I am Iqra and I treat your annotation guidelines as a contract. My passion for AI is reflected in my role as a Data Annotation Specialist with hands-on CVAT experience labeling images and video for computer vision models. I deliver pixel-accurate bounding boxes, polygons, and segmentation masks that train production-ready AI not "good enough" data that breaks your model in deployment. ✅ WHAT I ANNOTATE IMAGE ANNOTATION — Bounding boxes, polygons & polylines — Semantic & instance segmentation — Object detection & classification labeling — Landmark & keypoint annotation — Image tagging and categorization TEXT ANNOTATION — Named entity recognition (NER) — Sentiment analysis & intent labeling — Text classification & topic tagging AUDIO & VIDEO ANNOTATION — Transcription and speaker diarization — Audio event tagging & classification — Subtitle alignment and timestampin ✅ Tools I work with daily: CVAT • LabelBox • Label Studio • Roboflow • SuperAnnotate • V7 Labs • Amazon SageMaker Ground Truth • Encord ✅Who I work best with: — ML/AI startups building computer vision or NLP products — Companies running ongoing annotation pipelines who need a reliable long-term labeler — Teams doing RLHF or LLM evaluation work ✅How I work: — I start every project by reviewing your guidelines and annotating a small test batch (20-50 items) so you can verify quality before scaling — I document edge cases as I find them and ask clarifying questions in batches — not one-by-one interruptions → I deliver in your preferred format with a short QA summary noting any uncertain labels for your review — I'm available 30+ hours/week and respond to messages within a few hours during my workday. ✅What you actually get when you hire me: — 98%+ annotation accuracy verified through QA review cycles — Edge cases flagged and discussed — not silently guessed at — Consistent labeling logic across large datasets — Fast turnaround on bulk work without quality drop-off in the last 10% Send me your annotation guidelines and a sample batch — I'll return a labeled test set within 24 hours so you can verify accuracy before committing to a larger contract. Looking forward to helping you build training data your model can actually learn from. Iqra Akram

Imagine spending thousands of dollars training an AI model—only to realize the labels were flawed. That’s where I come in. I’m 𝐁𝐞𝐡𝐳𝐚𝐝 𝐀𝐥𝐢 𝐊𝐡𝐚𝐧 and Founder of 𝐒𝐖𝐀𝐓𝐀𝐢, a data annotation and AI development platform trusted by AI startups and enterprises worldwide. With 4+ years of experience and a team of 178+ skilled annotators, I’ve personally led projects that helped train over 100 production-grade AI models from medical image segmentation, agriculture dataset annotations, AI-based electric poles condition monitoring system to autonomous vehicle perception systems. 🌐𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗧𝗿𝘂𝘀𝘁 𝗠𝗲: 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗟𝗲𝘃𝗲𝗹 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: I specialize in polygonal, pixel-wise and semantic segmentation with an obsessive attention to detail. 𝗔𝗜-𝗔𝘄𝗮𝗿𝗲 𝗔𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻: Unlike generic labelers, I understand the AI pipeline. I don’t just label, I label for performance. Every dataset is annotated with model training, accuracy and edge-case handling in mind. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗦𝗰𝗮𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Delivered $500,000+ worth of labeled data to top AI companies (like Moonvalley, SaharLabs etc) with proven systems for scalability, security and deadlines. 𝗖𝘂𝘀𝘁𝗼𝗺 𝗧𝗼𝗼𝗹𝘀, 𝗥𝗲𝗮𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Worked across CVAT, Labelbox, SuperAnnotate, Roboflow and even client-specific proprietary tools. 🌐𝗦𝘁𝗼𝗿𝘆 𝗼𝗳 𝗚𝗿𝗼𝘄𝘁𝗵: What began as a solo freelancing gig on Fiverr has now become a global agency delivering AI-ready data for some of the most innovative companies on Earth. Now offering our services on Upwork as well. 🛠️𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗜 𝗢𝗳𝗳𝗲𝗿: ✦ Image & Video Segmentation (polygon, semantic, instance-based) ✦ Bounding Box, Keypoint & Landmark Annotation ✦ Text & Audio Annotation (multilingual available) ✦ Dataset Cleaning, Structuring & Preprocessing ✦ Consultancy on AI Dataset Design and Labeling Strategies ✦ AI Model Training and development Image annotation, Bounding boxes, 3D boxes, Video annotation, instance and semantic, Object labeling/tagging,Segmentation, Polygons masks, Text annotation, Line annotation, Key Points annotation, Cuboids, Image classification and categorization.

With over 5 years of experience I specialize in annotating and labeling dataset for machine learning and AI applications. With a proven track record in creating accurate high-quality datasets across diverse domains.

I build computer vision systems that turn raw images and video into automated decisions, detection, segmentation, OCR, tracking. Production-ready pipelines, not notebook demos. Most teams I work with have images or video piling up and no reliable automated way to turn it into decisions: flagging defects, counting inventory, verifying identity, extracting text from scanned documents. Off-the-shelf tools rarely fit the exact use case, and in-house attempts often stall somewhere between a proof of concept and a working product. What I do: - Object detection & tracking (YOLO, DETR, Faster R-CNN) - Image segmentation — semantic, instance, and panoptic - OCR & document understanding for real-world, messy scans - Face detection, recognition, and biometric verification - Video analytics — counting, re-identification, anomaly detection - Model optimization and edge deployment (Jetson, Coral, mobile) - Dataset curation, annotation pipeline design, and active learning Tech stack: Python, PyTorch, TensorFlow, OpenCV, Detectron2, MMDetection, Ultralytics YOLO, Segment Anything (SAM), ONNX, TensorRT, Docker, FastAPI, AWS/GCP/Azure. I work end-to-end — from raw data through training, evaluation, and deployment — and I care about models that hold up outside the training set, not just ones that score well on a benchmark. My process: a short discovery call to understand the problem, then a small proof-of-concept on a sample of your data before we scope the full build. That way you can see real results before committing to a bigger engagement. If you've got images or video and a decision you want automated, message me and let's talk through what's feasible for your data and timeline.