You will get Custom Computer Vision Models: Classification, Object Detection, Analysis
Project details
I build computer vision solutions that work on messy, real-world data - not just clean benchmarks.
WHY THIS MATTERS:
I achieved SOTA on COCO weakly-supervised segmentation (56% mIoU) - one of the hardest public datasets with 80+ classes. This proves I can handle your messy data, inconsistent labels, and edge cases that break typical models.
WHAT YOU GET:
Research-Grade Performance
• Beat published SOTA on COCO segmentation and ISIC medical imaging (94.5%)
• Specialized in learning from minimal labeled data - critical for real projects
Production Engineering
• Full-stack: API integration, deployment, error handling
• Not just models - complete solutions that actually ship
Business Focus
• Experience across CV, NLP, predictive modeling, AI agents
• Workflow optimization and revenue generation consultation
• Focus on ROI, not technical perfectionism
PRACTICAL ADVANTAGE:
Freelance projects have imperfect data: imbalanced classes, quality issues, inconsistent labels. My SOTA work on challenging datasets means I extract maximum performance from difficult real-world data - exactly what you need.
Research expertise + production skills + business understanding.
WHY THIS MATTERS:
I achieved SOTA on COCO weakly-supervised segmentation (56% mIoU) - one of the hardest public datasets with 80+ classes. This proves I can handle your messy data, inconsistent labels, and edge cases that break typical models.
WHAT YOU GET:
Research-Grade Performance
• Beat published SOTA on COCO segmentation and ISIC medical imaging (94.5%)
• Specialized in learning from minimal labeled data - critical for real projects
Production Engineering
• Full-stack: API integration, deployment, error handling
• Not just models - complete solutions that actually ship
Business Focus
• Experience across CV, NLP, predictive modeling, AI agents
• Workflow optimization and revenue generation consultation
• Focus on ROI, not technical perfectionism
PRACTICAL ADVANTAGE:
Freelance projects have imperfect data: imbalanced classes, quality issues, inconsistent labels. My SOTA work on challenging datasets means I extract maximum performance from difficult real-world data - exactly what you need.
Research expertise + production skills + business understanding.
Machine Learning Tools
MLflow, OpenCV, pandas, Python, Python Scikit-Learn, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$250
|
Standard
$600
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 2 | 3 | 4 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code | - | - |
Frequently asked questions
About Choi
AI Engineer: Vision, NLP, RL, Agent | SOTA Research meets Production
Seoul, South Korea - 4:37 am local time
CORE CAPABILITIES:
AI Agent Systems (LangGraph/LangChain)
- Multi-agent architectures with state management and tool orchestration
- Human-in-the-loop workflows for complex decision-making
- API integration and async processing for production deployment
- Real deployment: Travel booking system with 4 API integrations serving live customers
Computer Vision
- Weakly-supervised semantic segmentation (56% mIoU COCO, beat SOTA)
- Object detection and tracking for real-time applications
- Medical imaging classification (94.5% ISIC benchmark)
- Self-training, multi-signal fusion (CLIP/DINO), custom architectures
NLP & Document Intelligence
- Large-scale document classification and routing systems
- Weak supervision frameworks with minimal labeled data
- Information extraction and semantic matching
- Production system: Automated 270+ file processing (40 hours → 2 hours)
Predictive Modeling & RL
- Ensemble methods: XGBoost, NGBoost, LightGBM, CatBoost
- Uncertainty quantification for high-stakes decisions
- Reinforcement learning for optimization problems
- Real impact: 3x improvement in bid success rate (₩7.7T market)
Production Engineering
- Web automation with anti-bot evasion (Playwright, Selenium)
- Async architectures for long-running AI tasks (FastAPI, Node.js)
- Database design, API development, frontend integration (React)
- Deployment: Docker, AWS, GCP, monitoring and error handling
WHAT MAKES MY WORK DIFFERENT:
I don't just train models - I build complete systems where AI components work together to automate complex workflows. Whether it's combining computer vision with NLP for document understanding, or integrating predictive models into multi-agent systems, I focus on end-to-end solutions that deliver measurable business value.
DELIVERABLES:
✓ Production-ready code with proper error handling and logging
✓ Clear documentation and architecture decisions
✓ Model training pipelines with evaluation metrics
✓ Deployment guides and monitoring setup
✓ Real results, not benchmarks
TECHNICAL STACK:
AI/ML: PyTorch, TensorFlow, Transformers, LangChain, LangGraph
Models: XGBoost, LightGBM, CatBoost, NGBoost, RL frameworks
Backend: Python, FastAPI, Node.js, async processing
Frontend: React, JavaScript
Automation: Playwright, Selenium, API integration
DevOps: Docker, AWS, GCP, monitoring tools
Based in Seoul (UTC+9), flexible for US/EU hours.
GitHub: github.com/HarimxChoi
Available for: AI agent development, computer vision systems, NLP applications, predictive modeling, end-to-end ML pipelines, production automation
Let's discuss how AI can solve your specific business problem.
Steps for completing your project
After purchasing the project, send requirements so Choi can start the project.
Delivery time starts when Choi receives requirements from you.
Choi works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset Analysis & Approach Planning
Review your data quality, size, and labels. Identify challenges (class imbalance, small objects, etc). Recommend architecture and set realistic expectations. If issues found, suggest improvements before training.
Model Development
Train model with data augmentation. Experiment with architectures and hyperparameters. Monitor metrics and adjust approach. Optimize for your specific requirements (speed vs accuracy trade-off).