Freelancers using Cvat

Freelancers using CvatFreelancers using Cvat

Projects People

Prashant Chaudhary

Helping companies build AI with high-quality data

Helping companies build AI with high-quality data

Cover image for Annotated computer vision datasets used

Annotated computer vision datasets used for vehicle detection and Automatic Number Plate Recognition (ANPR) systems. The project involved labeling vehicles and number plates in traffic camera footage to help train AI models used in smart city infrastructure, traffic monitoring, and law enforcement systems. Key responsibilities included: • Vehicle detection using bounding box annotation • Number plate annotation for ANPR systems • Dataset preparation for computer vision models • Annotation quality validation and dataset review These datasets support AI models used in traffic monitoring systems, automated toll collection, smart parking systems, and urban mobility analytics.

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Cover image for Annotated CT scan medical imaging

Annotated CT scan medical imaging datasets used to train AI models for healthcare diagnostics and medical image analysis. The project involved identifying and labeling anatomical regions within CT scan images using bounding boxes and segmentation techniques to support machine learning model training. Key tasks included: • Medical image annotation and region labeling • Bounding box annotation for anatomical structures • Dataset preparation for AI model training • Quality control and annotation validation This work supports the development of AI systems used in healthcare diagnostics, medical imaging analysis, and clinical decision support systems.

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Cover image for Annotated complex dental X-ray medical

Annotated complex dental X-ray medical imaging datasets used to train AI models for healthcare applications. The project involved precise labeling of anatomical structures including teeth boundaries, roots, and surrounding regions using polygon and keypoint annotation techniques. The objective was to generate high-quality training datasets that allow machine learning systems to accurately detect dental structures and support medical imaging analysis. Responsibilities included: • Image segmentation and polygon annotation • Keypoint labeling for anatomical structures • Dataset preparation for machine learning models • Annotation quality control and validation This work contributes to the development of AI systems used in medical diagnostics and healthcare imaging analysis.

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Cover image for Worked on Automatic Speech Recognition

Worked on Automatic Speech Recognition (ASR) dataset annotation used for training speech-to-text AI models. The project involved labeling and segmenting audio recordings, identifying speakers, and preparing structured datasets for machine learning systems. Key responsibilities included: • Audio segmentation and timestamp labeling • Speaker identification and classification • Speech-to-text dataset preparation • Annotation quality validation and review This dataset helps train AI systems used in voice assistants, speech recognition software, and conversational AI applications.

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Sanket Sabharwal, PhD

maxGenoa, Italy

Senior Software & ML Engineer | Zero to One Product Builder

6x: Hired

5.0: Rating

60: Followers

Top

Expert

Senior Software & ML Engineer | Zero to One Product Builder

Cover image for Computer Vision for Manufacturing - Defect Detection & QA

Computer Vision for Manufacturing - Defect Detection & QA

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Cover image for Web Scraping Systems - Large-Scale Data Extraction Pipelines

Web Scraping Systems - Large-Scale Data Extraction Pipelines

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Cover image for BI Dashboards - Retail Analytics & Forecasting

BI Dashboards - Retail Analytics & Forecasting

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Cover image for Machine Learning for Recommendation Systems - Personalization

Machine Learning for Recommendation Systems - Personalization

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Svetlana Rumyantseva

Panama City, Panama

Founder & AI Systems Architect

New to Contra

Founder & AI Systems Architect

Cover image for Horse Health AI is a

Horse Health AI is a Golden Dragon AI DeepTech project for portable, non-invasive, multimodal animal health screening and longitudinal health intelligence. I designed the system around a compact multimodal sensor architecture combining RGB Computer Vision, multi-wavelength NIR, radiometric thermal imaging, structured/polarized optical sensing, and motion and geometry data. Rather than analyzing each signal independently, the AI architecture is designed to fuse complementary modalities and evaluate visible anatomy, gait and behavior, thermal patterns, vascular and perfusion-related features, tissue-related optical signals, movement, and individual history. A key part of the architecture is the Individual Digital Baseline. Each animal can become its own longitudinal reference, allowing new scans to be compared not only with validated population-level knowledge but also with that animal's historical normal patterns. The software architecture includes synchronized acquisition, calibration, multimodal registration, per-modality AI models, longitudinal baseline modeling, multimodal embeddings and fusion, veterinary knowledge/RAG, explainable anomaly localization, confidence and uncertainty assessment, and mobile and research interfaces. The project is designed as a screening and decision-support system, not as a replacement for veterinary diagnosis. Veterinary expertise, clinically confirmed data and validation are explicit parts of the development roadmap. The initial platform is focused on horses, with potential expansion to other animal species following species-specific validation.

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Cover image for Enterprise Multimodal AI Platforms (NDA)
Designed

Enterprise Multimodal AI Platforms (NDA) Designed and developed two production-grade enterprise AI platforms under NDA, taking ownership of the complete AI lifecycle from research and system architecture to production deployment and long-term platform support. The platforms integrated LLMs, RAG, Computer Vision, OCR, intelligent document processing, AI agents, custom neural networks, benchmarking, model evaluation, dataset engineering, synthetic data generation, model training, fine-tuning, optimization, and scalable production inference pipelines.

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Cover image for Food as Healing AI is

Food as Healing AI is a personal AI nutrition and food intelligence platform designed around the individual. The platform builds a persistent Personal Nutrition Profile from the user's goals, food habits, lifestyle, preferences, restrictions, ingredient policies, practical constraints, feedback, and optional user-provided laboratory context. Its food intelligence pipeline uses Vision/OCR, ingredient extraction and normalization, an ingredient and additive knowledge base, RAG, and structured product analysis to help users understand what is actually represented in a food product. A Personal Matching Engine is designed to connect structured product information with each user's individual food policy — including hard exclusions, preferences, dietary patterns, nutrition goals, clean-label requirements, and ingredient rules. The broader architecture is modular and multi-agent. Specialized agents are designed for personal profiles, questionnaires, nutrition, ingredients, products, matching, shopping, meal planning, knowledge retrieval, and continuous feedback. The current technical foundation includes a Google Cloud backend, Supabase, RAG, prompt/AI orchestration, external AI model integration, an ingredient/additive knowledge base, and an image-based ingredient analysis prototype. The goal is not to provide another generic diet app or diagnostic system, but to create a personal AI food consultant that learns how an individual wants to eat and turns that profile into explainable everyday food decisions.

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Cover image for Golden Dragon Quantum Trading
AI-Native Multi-Agent

Golden Dragon Quantum Trading AI-Native Multi-Agent Intelligence for Active Crypto Trading Golden Dragon Quantum Trading is a highly customizable AI-native crypto trading intelligence and controlled execution platform. I designed the platform around a distributed multi-agent architecture: the trader communicates with one Main AI, while a network of specialized intelligent AI agents analyzes different market domains in parallel, including market structure, order books and liquidity, derivatives, volume and flow, whale activity, cross-exchange conditions, news, risk, and trading strategies. The architecture combines personalized trading strategies, manipulation and anomaly detection, whale and hidden-liquidity intelligence, multi-agent confirmation, hybrid quantum-classical computation, and a proprietary orchestration and real-time communication architecture. A separate System Control Core supervises system state and permissions, while Protective Gates validate risk, strategy, market, exchange, and execution conditions before authorized actions reach the Execution Robot. The platform is designed for multi-exchange operation, including Binance, Bybit, Bitget, OKX and Kraken. User funds remain in the trader’s own exchange accounts. I also built the customer-facing product environment, including registration, authentication, subscriptions, payments, personal access tokens, exchange connection, multi-exchange selection, TradingView integration, multilingual functionality, and the Main AI trading workspace. My role: Founder, AI Systems Architect & Developer Project: Architecture, AI/ML, Multi-Agent Systems, Trading Intelligence, Backend, Cloud, Product Development

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Web Designer | UI/UX Designer| Graphic Designer |

New to Contra

Web Designer | UI/UX Designer| Graphic Designer |

Cover image for 🚀 Excited to Continue Growing

🚀 Excited to Continue Growing in AI Data Annotation & Quality Assurance Over the past months, I’ve had the opportunity to work on various data annotation and quality assurance projects involving image and video datasets. Using tools such as CVAT and Labelbox, I’ve gained valuable experience in bounding boxes, object tracking, segmentation, data validation, and annotation review. Working on AI training datasets has strengthened my attention to detail, problem-solving abilities, and commitment to delivering high-quality results. Every project provides a new opportunity to learn and contribute to the development of more accurate and reliable AI systems. I’m always interested in connecting with professionals in AI, machine learning, computer vision, and data annotation. Feel free to reach out if you’d like to connect, collaborate, or discuss opportunities in this exciting field. #AI #DataAnnotation #QualityAssurance #ComputerVision #MachineLearning #CVAT #Labelbox #ArtificialIntelligence #DataLabeling #RemoteWork

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Cover image for Project: Football Video Annotation and

Project: Football Video Annotation and Quality Assurance✅ Reviewing annotations to ensure consistency and compliance with project guidelines ✅ Identifying and correcting labeling errors ✅ Maintaining high-quality standards across large video datasets ✅ Supporting the development of computer vision models through reliable training data Projects like this continue to strengthen my expertise in data annotation, quality assurance, and AI training workflows. It is rewarding to contribute to datasets that help improve the performance of machine learning and computer vision systems. I’m always open to connecting with professionals and organizations working in AI, computer vision, data annotation, and quality assurance.

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Cover image for Q. How do you ensure

Q. How do you ensure annotation quality? A. Every dataset is reviewed for accuracy and consistency before delivery, following your labeling guidelines.

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Cover image for Created a full e-commerce website

Created a full e-commerce website UI design for Burger Hub, a premium burger restaurant. The project included a homepage, featured menu, about/story page, contact page and footer. Designed in Figma with a bold dark theme, red accents and modern food e-commerce layout." Skills to tag: Website Design UI/UX Design Figma E-Commerce Design Restaurant Website

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Founder at NextGen Annotator | AI Data Annotation | QA.

New to Contra

Founder at NextGen Annotator | AI Data Annotation | QA.

Cover image for 🚀 Scaling Your AI Models

🚀 Scaling Your AI Models or Data Operations? Let Our Team of 40 Handled the Heavy Lifting! Hey Contra Community! 👋 Are you struggling to keep up with massive datasets, messy annotations, or slow image verification pipelines? If you are an AI startup, tech agency, or enterprise looking for a reliable partner to handle high-volume data tasks, we are here to help! I manage a structured, fully trained team of 40 data annotation and QA specialists ready to deploy immediately. ⚙️ What We Bring to the Table: Massive Volume, Fast Delivery: With 40 people working in sync, we can process thousands of images, posts, or data rows daily without breaking a sweat. Flawless Quality Control: We don’t just annotate; we implement a strict internal QA layer to ensure 99%+ accuracy before final delivery. Versatile Expertise: Experienced in Image QA/Review (like matching AI outputs to inputs), Data Labeling (Bounding boxes, tagging), and Large-scale Data Entry (CSV/Sheets management). 💼 Flexible Setup: Whether you have a 1-week urgent sprint or need an ongoing monthly workforce, we scale according to your project requirements. Let's remove the bottleneck from your operations. DM me directly to discuss your project requirements or to set up a quick sample/pilot task! Let’s build something great together. 📈

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Cover image for 🚀 Looking for Large-Scale Data

🚀 Looking for Large-Scale Data Annotation or Bulk Image QA Projects! Hello Contra Community! 👋 I manage a highly efficient, dedicated team of 40 experienced data annotation and visual QA specialists. We are fully equipped to handle bulk workloads with top-tier accuracy and fast turnaround times. What We Specialize In: 🖼️ Image QA & Review: Evaluating AI-generated outputs against source images (bounding boxes, tagging, and multi-outcome classification). 🏷️ Data Annotation & Labeling: Large-scale categorization, metadata tagging, and object recognition. 📋 Bulk Data Entry & Verification: Managing and organizing massive datasets seamlessly into structured formats (CSV, Sheets, etc.). Why Partner With Us? Scalability: With 40 trained specialists, we can scale up immediately to meet tight deadlines and high volumes. Quality Assurance: Dual-layer review process to ensure 99%+ accuracy before final delivery. Flexible Models: Ready for short-term pilot tasks or long-term ongoing contract operations. If your company, AI startup, or agency is looking to outsource large annotation or image vetting pipelines, let’s connect! DM me directly or invite us to your project. 💼 My email: k.nazish2924@gmail.com (mailto:k.nazish2924@gmail.com)

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Designed a premium brand wallpaper for Oz Golden Rush using Meta AI for custom assets and Canva for professional typography. The design focuses on a luxury aesthetic (Gold & Swiss watches) to align with the brand's identity as Australia's first mobile pawn broker.

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Cover image for "I just finished building this

"I just finished building this Dynamic Task Tracker in Excel! 📊 To make project management easier, I’ve added: ✅ Visual Status: Green/Yellow/Red indicators for instant updates. ✅ Progress Bars: Automated bars to show task completion at a glance. ✅ Organized Workflow: Structured by priority and deadlines. If you need help organizing your business data or creating custom Excel dashboards, I’m available for new projects! 🚀"

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Bhimrao Yamulwad

AI-Powered Digital Solutions Specialist Web Design ·Content

New to Contra

AI-Powered Digital Solutions Specialist Web Design ·Content

Cover image for AI Data Annotation & Data

AI Data Annotation & Data Labeling Portfolio

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Cover image for Travel Booking Website – UI/UX Design

Travel Booking Website – UI/UX Design

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Cover image for Juhi AI – Autonomous AI Voice Assistant 🤖🎙️

Juhi AI – Autonomous AI Voice Assistant 🤖🎙️

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Cover image for Audio & Video Transcription Portfolio

Audio & Video Transcription Portfolio

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Francis Anyaora

AI Data Specialist | PDF → Markdown | RAG & Vector Databases

AI Data Specialist | PDF → Markdown | RAG & Vector Databases

Cover image for High-Throughput Data Annotation & Quality

High-Throughput Data Annotation & Quality Assurance: Delivered rigorous video annotation, data labeling, and quality assurance for advanced AI training models during my time working with Turing. I maintained strict adherence to complex labeling guidelines, ensured high daily processing throughput, and applied precision quality control to eliminate formatting noise and edge-case errors before model training.

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Cover image for Hierarchical Markdown Structure for Vector

Hierarchical Markdown Structure for Vector Embeddings: Messy document layouts destroy RAG retrieval accuracy. In this project, I designed a strict Markdown heading hierarchy (#, ##, ###) specifically optimized for text chunking. By programmatically maintaining parent-child document relationships, I ensured that semantic search algorithms in the vector store can accurately retrieve the exact context an LLM needs, drastically reducing hallucinations.

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Cover image for Custom Regex Library for Automated

Custom Regex Library for Automated Text Cleanup & Restructuring: Automated layout parsers often leave behind substantial structural noise. When converting dense legal text, court rulings, and statutes, you routinely encounter broken line wraps, random line breaks in the middle of sentences, ghost characters, and inconsistent citation layouts. Manually editing thousands of pages is impossible, yet feeding this raw noise into an LLM degrades context window efficiency.

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Cover image for Legal Document Conversion & Markdown

Legal Document Conversion & Markdown Optimization: This is the flagship personal project. It focuses on taking raw, messy, multi-column statutes and court rulings and turning them into clean Markdown.

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A specialist offering data annotation services.

A specialist offering data annotation services.

Cover image for Precision Data Annotation for AV Perception

Precision Data Annotation for AV Perception

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Cover image for Marine Data Annotation

Marine Data Annotation

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Cover image for Data Annotation Services for Multiple Clients

Data Annotation Services for Multiple Clients

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