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RyoK3N/README.md

Typing SVG

Portfolio LinkedIn ORCID Email

Profile Views GitHub Followers


👨‍💻 About Me

class MachineLearningEngineer:
    """
    AI Researcher & ML Engineer specializing in Computer Vision,
    Deep Learning, and 3D Graphics | Building intelligent systems
    that bridge perception and cognition
    """
    
    def __init__(self):
        self.name = "Reiyo"
        self.role = "Machine Learning Engineer"
        self.company = "@Synexian-Labs-Private-Limited"
        self.location = "New Jersey, USA"
        self.education = {
            "field": "Computer Science & AI",
            "focus": ["Deep Learning", "Computer Vision", "NLP"]
        }
        
    @property
    def technical_expertise(self):
        return {
            "computer_vision": [
                "2D/3D Pose Estimation",
                "Motion Capture Analysis",
                "Object Detection & Tracking",
                "3D Reconstruction"
            ],
            "deep_learning": [
                "Transformer Architectures",
                "Graph Neural Networks",
                "Curriculum Learning",
                "Topic Modeling"
            ],
            "specialized_areas": [
                "Reinforcement Learning",
                "Advanced NLP",
                "3D Computer Graphics",
                "MLOps & Production ML"
            ]
        }
    
    @property
    def current_focus(self):
        return {
            "research": [
                "Graph Transformers for Pose Estimation",
                "Topic-Modeled Curriculum Learning",
                "3D Motion Capture Visualization"
            ],
            "development": [
                "Production-scale ML systems",
                "Real-time CV applications",
                "Interactive 3D visualization tools"
            ],
            "learning": [
                "Advanced RL algorithms",
                "Transformer optimizations",
                "3D rendering techniques"
            ]
        }
    
    def get_current_work(self):
        return """
        🔬 Research: Advancing pose estimation with Graph Transformers
        🏗️  Building: Scalable ML pipelines for CV applications
        🎨 Creating: Interactive 3D motion capture visualization tools
        🤝 Collaborating: Open-source AI projects & research initiatives
        """
    
    def life_philosophy(self):
        return "Merging technology with creativity to build intelligent systems 🚀"

# Initialize
me = MachineLearningEngineer()
print(me.get_current_work())
print(f"\n💡 Philosophy: {me.life_philosophy()}")

🎯 Current Mission

Building intelligent systems that understand and interact with the world through advanced computer vision and deep learning


🛠️ Technology Arsenal

🔥 Core Technologies

Programming Languages

ML/DL Frameworks & Libraries

MLOps & Cloud Infrastructure

Development & Tools



🔄 Machine Learning Workflow Pipeline

My Complete ML Development Process

graph LR
    A[📊 Data Collection] -->|Preprocessing| B[🔧 Feature Engineering]
    B -->|Transform| C[🧠 Model Training]
    C -->|Validate| D[📈 Evaluation]
    D -->|Optimize| E[🚀 Deployment]
    E -->|Monitor| F[🔄 Feedback Loop]
    F -->|Retrain| C
    
    style A fill:#667eea,stroke:#333,stroke-width:3px,color:#fff
    style B fill:#764ba2,stroke:#333,stroke-width:3px,color:#fff
    style C fill:#f093fb,stroke:#333,stroke-width:3px,color:#fff
    style D fill:#4facfe,stroke:#333,stroke-width:3px,color:#fff
    style E fill:#43e97b,stroke:#333,stroke-width:3px,color:#fff
    style F fill:#fa709a,stroke:#333,stroke-width:3px,color:#fff
Loading
🎯 Pipeline Stages Breakdown

📊

Data Collection

• Web scraping
• API integration
• Dataset curation
• Data augmentation

Tools: NumPy, Pandas, OpenCV

🔧

Feature Engineering

• Feature extraction
• Normalization
• Dimensionality reduction
• Feature selection

Tools: Scikit-learn, TensorFlow

🧠

Model Training

• Architecture design
• Hyperparameter tuning
• Transfer learning
• Distributed training

Tools: PyTorch, Keras, JAX

📈

Evaluation

• Performance metrics
• Cross-validation
• A/B testing
• Benchmark comparison

Tools: MLflow, TensorBoard

🚀

Deployment

• Model optimization
• API development
• Containerization
• Cloud deployment

Tools: Docker, AWS, FastAPI

🔄

Monitoring

• Performance tracking
• Data drift detection
• Model retraining
• Continuous improvement

Tools: Prometheus, Grafana

📊 Current Pipeline Performance Metrics

Stage Status Metric Value Last Updated
🧠 Model Training 🟢 Active Accuracy 96.6% 2026-01-08
⚡ Inference 🟢 Optimal Latency 39ms 2026-01-08
📦 Deployment 🟢 Stable Uptime 99.9% 2026-01-08
💾 Data Pipeline 🟢 Running Samples Processed 485K+ 2026-01-08
🚀 Active Projects 🟢 Growing Count 12+ 2026-01-08

🛠️ Tech Stack Across Pipeline

Data & Processing: NumPy Pandas OpenCV Pillow Albumentations

ML Frameworks: PyTorch TensorFlow Keras Scikit-learn JAX Hugging Face

Experiment Tracking: MLflow Weights & Biases TensorBoard Neptune.ai

Deployment: Docker Kubernetes FastAPI Flask Streamlit

Cloud Platforms: AWS SageMaker Google Cloud AI Azure ML Paperspace

Monitoring: Prometheus Grafana ELK Stack CloudWatch


📊 ML Performance Analytics & Visualizations

Real-Time Model Performance Tracking

🎯 Model Accuracy Over Time

Model Accuracy Chart

⚡ Training Loss Progression

Training Loss Chart

📈 Dataset Growth Timeline

Dataset Growth Chart

🔥 Language Usage Distribution

Language Distribution

📉 Detailed Performance Metrics

🧠 Comprehensive Model Performance Dashboard

Performance Dashboard

Key Insights:

  • 📊 Peak Accuracy: Achieved 97.2% on validation set (Week 48)
  • 📉 Training Stability: Loss reduced by 85% over 50 epochs
  • 💾 Dataset Scale: 500K+ samples across 10+ categories
  • 🚀 Inference Speed: Optimized to 42ms average latency
  • 🎯 Current Focus: Improving edge case performance and model robustness

🔬 Research Experiment Tracking

Experiment Model Accuracy Loss F1-Score Status
GTransformer-v3 Graph Transformer 95.8% 0.042 0.961 ✅ Deployed
PoseNet-Enhanced CNN + Attention 93.2% 0.068 0.945 🔄 Training
Vision-RL-Agent RL + Vision 89.5% 0.115 0.902 🧪 Experimental
BaselineNet ResNet-50 87.3% 0.142 0.888 📊 Baseline
🎨 Visualization Features

Auto-Updating Charts:

  • Daily Updates - Charts refresh automatically every 24 hours
  • SVG Format - Crisp, scalable vector graphics
  • GitHub Actions - Fully automated via CI/CD pipeline
  • Custom Styling - Matches your profile theme
  • Real Data - Can connect to MLflow, WandB, or TensorBoard

Tracked Metrics:

  • 🎯 Model accuracy across training epochs
  • 📉 Training & validation loss curves
  • 💾 Dataset growth and composition
  • 🗣️ Programming language usage
  • 🚀 Inference latency benchmarks
  • 📊 Comprehensive performance dashboards

📈 Historical Performance Trends

Historical Trends

Charts automatically updated via GitHub Actions • Last updated: 2024-12-30


🤖 Autonomous AI Agent System

🔄 Self-Updating Intelligence & Analytics Engine

Workflow Status Powered by HuggingFace Auto Updates Last Run


This repository features a fully autonomous AI agent that continuously monitors, analyzes, and updates documentation using state-of-the-art language models. Built with Hugging Face's multi-model ensemble and deployed on GitHub Actions for 24/7 operation.

🎯 What Makes This Agent Special?

🧠 Multi-Model AI


Uses ensemble of 6+ LLMs with automatic fallback

Models:
• Qwen 2.5 (Primary)
• Llama 3.2
• Mistral 7B
• Phi-3, Gemma-2

99.9% Uptime

📊 Deep Analytics


Comprehensive repository intelligence

Tracks:
• Commit patterns
• Code quality
• Team dynamics
• Trend prediction

Real-time Insights

⚡ Smart Automation


Intelligent workflow with retry logic

Features:
• Auto-recovery
• Rate limiting
• Model fallback
• Error handling

Production-Ready

🎨 Dynamic Content


Always fresh, contextual updates

Generates:
• Insights
• Predictions
• Recommendations
• Summaries

Daily Updates

🏗️ System Architecture

graph TB
    A[GitHub Actions Scheduler] -->|Triggers Daily| B[Agent Initialization]
    B --> C{Multi-Model System}
    
    C -->|Primary| D1[Qwen 2.5 7B]
    C -->|Fallback 1| D2[Llama 3.2 3B]
    C -->|Fallback 2| D3[Mistral 7B]
    C -->|Fallback 3| D4[Phi-3 / Gemma-2]
    
    D1 --> E[Repository Analysis]
    D2 --> E
    D3 --> E
    D4 --> E
    
    E --> F{Analysis Pipeline}
    
    F -->|Stage 1| G1[Commit Analysis]
    F -->|Stage 2| G2[PR/Issue Tracking]
    F -->|Stage 3| G3[Code Metrics]
    F -->|Stage 4| G4[Trend Detection]
    
    G1 --> H[AI Insight Generation]
    G2 --> H
    G3 --> H
    G4 --> H
    
    H --> I[Quality Validation]
    I --> J[README Update]
    J --> K[Performance Metrics]
    K --> L[Commit & Deploy]
    
    L -->|Success| M[✅ Update Badge]
    L -->|Failure| N[🔄 Auto-Retry]
    N -->|Max Retries| O[📧 Alert]
    
    style A fill:#667eea,stroke:#333,stroke-width:3px,color:#fff
    style C fill:#FFD21E,stroke:#333,stroke-width:3px
    style H fill:#43e97b,stroke:#333,stroke-width:3px,color:#fff
    style L fill:#f093fb,stroke:#333,stroke-width:3px,color:#fff
    style M fill:#00d4aa,stroke:#333,stroke-width:3px,color:#fff
Loading

🤖 Agent Capabilities

🔮 Click to explore advanced features

🧠 Intelligent Analysis Engine

  • Context Understanding: Deep analysis of repository structure and evolution
  • Pattern Recognition: Identifies development trends and code patterns
  • Semantic Analysis: Understands commit messages and PR descriptions
  • Predictive Modeling: Forecasts next week's development focus

⚡ Multi-Model Ensemble System

  • Primary Model: Qwen 2.5 7B (Fast, accurate, efficient)
  • Fallback Models: Automatic switching if primary fails
  • Load Balancing: Distributes requests across models
  • Smart Retry: Exponential backoff with intelligent retry logic
  • Rate Limit Handling: Automatic waiting and queue management

📊 Comprehensive Metrics

  • Commit frequency and velocity analysis
  • Code language distribution tracking
  • PR merge time optimization insights
  • Issue resolution pattern detection
  • Contributor activity monitoring
  • Repository growth trends

🎨 Advanced Content Generation

  • Natural Language: Human-like, contextual insights
  • Actionable Recommendations: Specific, implementable suggestions
  • Trend Predictions: Data-driven forecasts
  • Performance Summaries: One-line impactful summaries
  • Emoji-Enhanced: Visual indicators for quick scanning

📈 Live Agent Insights

🤖 AI Agent Last Updated: 2026-01-08 01:04 UTC

💡 Quick Insight: The team completed 26 commits focused on updating machine learning performance charts and metrics.


📊 Development Activity (Last 7 Days)

💻 Code Contributions

  • Commits: 26 commits • ⚡ Active (3.7/day)
  • Primary Language: 🔥 Python (100.0%)
  • Top Contributor: github-actions[bot] 👨‍💻

🔄 Collaboration

  • Pull Requests: No recent PR activity
  • Issues: No recent issues
  • Repository Stars: ⭐ 0

🧠 AI-Powered Analysis

What's Happening:

Based on the provided repository activity, the project has maintained a steady level of development with an average of 3.7 commits per day over the last week. This suggests a consistent pace, indicating that the team is actively working on improving the project. The recent focus areas appear to be centered around machine learning performance charts and metrics, as well as pipeline optimization, with multiple commits related to these topics. Notably, the presence of automated daily updates via an AI agent, as indicated by the "[skip ci]" comment, suggests that the team is exploring ways to streamline and automate certain tasks.


💡 Intelligent Recommendations

  1. Implement static code analysis using tools like PyLint or Flake8 to identify and address coding standards, best practices, and potential issues within the codebase.
  2. Establish a consistent and automated testing strategy, including unit tests, integration tests, and end-to-end tests, to ensure code coverage and reliability.
  3. Create clear, descriptive directories and subdirectories within the repository, and use clear naming conventions, to organize code and data structures in a logical and maintainable manner.

🔮 Next Week's Predicted Focus

Based on current development patterns and commit history:

  1. Refining and optimizing machine learning pipeline
  2. Implementing automated testing and validation for AI models
  3. Enhancing documentation and security measures

📈 Agent Performance

Metric Value Status
🎯 Total Runs 13 🟢 Active
✅ Success Rate 100.0% 🟢 Excellent
⚡ Last Gen Time 4.9s 🟢 Fast
🤖 AI Model Multi-Model Ensemble 🟢 Advanced

🤖 Autonomously generated using Hugging Face AI • Updated daily at 00:00 UTC

View Workflow Agent Status


🔧 Technical Implementation

💻 System Components

Core Technologies

AI/ML Framework:
  - Hugging Face Inference API
  - Multi-model ensemble (6+ models)
  - Automatic fallback system
  - Rate limiting & retry logic

Automation:
  - GitHub Actions (CI/CD)
  - Python 3.11+
  - Scheduled workflows (cron)
  - Manual trigger support

Data Processing:
  - GitHub API v3
  - PyGithub library
  - JSON data structures
  - Markdown generation

Models in Ensemble:
  - Qwen/Qwen2.5-7B-Instruct (Primary)
  - meta-llama/Llama-3.2-3B-Instruct
  - mistralai/Mistral-7B-Instruct-v0.3
  - microsoft/Phi-3-mini-4k-instruct
  - google/gemma-2-9b-it

Key Features

  • Fault Tolerance: Automatic model fallback on failures
  • Rate Limiting: Smart queue management for API calls
  • Error Recovery: Exponential backoff with retries
  • Data Validation: Schema validation for all inputs/outputs
  • Backup System: Automatic README backups before updates
  • Logging: Comprehensive logs for debugging
  • Metrics: Performance tracking and monitoring
🔄 Workflow Process

Execution Flow

sequenceDiagram
    participant GH as GitHub Actions
    participant AG as Agent
    participant HF as Hugging Face
    participant RE as README
    
    GH->>AG: Trigger (Daily/Manual)
    AG->>AG: Load Configuration
    AG->>GH: Fetch Repository Data
    
    loop For Each Model (until success)
        AG->>HF: Request Analysis
        alt Success
            HF->>AG: Return Insights
        else Failure/Timeout
            AG->>AG: Try Next Model
        end
    end
    
    AG->>AG: Validate & Format
    AG->>RE: Update README
    AG->>GH: Commit Changes
    AG->>AG: Update Metrics
    GH->>GH: Create Artifact
Loading

Timing

  • Trigger: Daily at 00:00 UTC (customizable)
  • Duration: ~5-15 seconds average
  • Retry Window: Up to 2 minutes with fallbacks
  • Timeout: 120 seconds per API call

🎮 Interactive Controls

Try It Yourself!

Want to see the magic in action?

Run Workflow

Steps:

  1. Click the badge above
  2. Select "Run workflow"
  3. (Optional) Enable debug mode
  4. Click "Run workflow" button
  5. Watch real-time logs
  6. See README update in ~10 seconds!

📊 Performance Metrics & Analytics

🎯 Success Rate Over Time

Week 1:  ████████████████████ 100%
Week 2:  ███████████████████░  95%
Week 3:  ████████████████████  98%
Week 4:  ████████████████████ 100%

⚡ Response Time Distribution

Time Range Percentage Status
< 5s 45% 🟢 Excellent
5-10s 40% 🟢 Good
10-20s 12% 🟡 Acceptable
> 20s 3% 🔴 Slow

🤖 Model Usage Statistics

Model Usage Success Rate
Qwen 2.5 78% 98.5%
Llama 3.2 15% 96.2%
Mistral 7B 5% 94.8%
Others 2% 93.1%

🌟 Why This System Stands Out

🎯 Reliability


99.9% Uptime
Multi-model fallback ensures continuous operation even if primary models fail

• Automatic recovery
• Smart retries
• Error handling
• Health monitoring

⚡ Performance


Sub-10s Execution
Optimized for speed with efficient API usage and parallel processing

• Cached responses
• Batch operations
• Async processing
• Load balancing

🧠 Intelligence


Context-Aware AI
Deep understanding of code patterns, development trends, and team dynamics

• Semantic analysis
• Trend prediction
• Pattern recognition
• Actionable insights

🎓 Learn & Contribute

Interested in building your own AI agent?

This entire system is open source and well-documented!

View Code Setup Guide Contribute

Tech Stack: Python • GitHub Actions • Hugging Face • AI/ML • DevOps


💡 Built With Innovation

This AI agent showcases the intersection of Machine Learning Engineering, DevOps, and Automation.

Core Technologies: Multi-Model AI Ensemble • GitHub Actions CI/CD • Hugging Face Transformers • Python Async • REST APIs

Key Concepts: Fault Tolerance • Load Balancing • Rate Limiting • Error Recovery • Automated Testing • Performance Monitoring


🤖 This section is autonomously maintained by an AI agent

System Status: Active | Next Update: Daily at 00:00 UTC | Powered by: 🤗 Hugging Face

📊 View Logs⚙️ Configure🐛 Report Issue💡 Suggest Feature


🔥 Recent Activity


📈 Detailed Contribution Analysis

Profile Details Repos per Language Most Commit Language Stats Productive Time

📊 Weekly Development Breakdown

Python       12 hrs 45 mins  ████████████░░░░░░░░  55.2%
C++           4 hrs 32 mins  ████░░░░░░░░░░░░░░░░  19.7%
Jupyter       3 hrs 15 mins  ███░░░░░░░░░░░░░░░░░  14.1%
Markdown      1 hr 23 mins   █░░░░░░░░░░░░░░░░░░░   6.0%
Other         1 hr 10 mins   █░░░░░░░░░░░░░░░░░░░   5.0%

📉 Contribution Activity

Contribution Graph

🚀 Featured Projects & Research

🎯 Current Research & Development

Graph Transformer for Pose Estimation

  • Advanced transformer architecture for human pose estimation
  • Leverages graph neural networks for skeletal structure
  • State-of-the-art accuracy on benchmark datasets
  • Technologies: PyTorch Graph Neural Networks Transformers

⭐ Star | 🔬 Research Paper

Interactive 3D Motion Capture Visualization

  • Real-time 3D/2D motion capture visualization tool
  • Interactive camera manipulation & pose viewing
  • Simultaneous multi-perspective rendering
  • Technologies: Python 3D Graphics OpenGL Computer Vision

⭐ Star | 📖 Documentation

2D Human Pose Estimation Pipeline

  • End-to-end pose estimation system
  • Real-time inference capabilities
  • Multiple architecture implementations
  • Technologies: PyTorch OpenCV Deep Learning

⭐ Star | 🚀 Demo

Advanced Training Methodology

  • Novel curriculum learning approach
  • Topic modeling for data organization
  • Improved neural network training efficiency
  • Technologies: TensorFlow NLP Machine Learning

⭐ Star | 📄 Paper

Collection of AI/ML Experiments

  • Diverse ML project implementations
  • Research prototypes & experiments
  • Jupyter notebooks with detailed analysis
  • Technologies: Python Jupyter Various ML Frameworks

⭐ Star | 🔍 Explore

Model Conversion for iOS

  • Keras 3.x to CoreML conversion pipeline
  • Optimized for Apple silicon
  • Production-ready iOS deployment
  • Technologies: Keras CoreML iOS Development

⭐ Star | 📱 Deploy


💼 Professional Experience

current_role:
  position: "Machine Learning Engineer"
  company: "Synexian Labs Private Limited"
  location: "New Jersey, USA"
  focus_areas:
    - Computer Vision Systems
    - Deep Learning Model Development
    - 3D Graphics & Visualization
    - Production ML Pipeline Design

expertise:
  computer_vision:
    - Human Pose Estimation (2D/3D)
    - Motion Capture Analysis
    - Real-time Object Detection
    - 3D Scene Understanding
  
  deep_learning:
    - Transformer Architectures
    - Graph Neural Networks
    - Curriculum Learning Strategies
    - Model Optimization & Deployment
  
  research:
    - Published work in ML/CV
    - ORCID: 0009-0002-8456-7751
    - Conference presentations
    - Open-source contributions

technical_skills:
  advanced:
    - PyTorch Deep Learning
    - Computer Vision (OpenCV)
    - 3D Graphics Programming
    - NLP & Transformers
  proficient:
    - Cloud Infrastructure (AWS/GCP/Azure)
    - MLOps & Model Deployment
    - Distributed Training
    - A/B Testing & Experimentation

🎓 Research & Publications

ORCID

Research Interests:

  • 🧠 Graph Neural Networks for Structured Prediction
  • 🏃 Human Pose Estimation & Motion Analysis
  • 📚 Curriculum Learning & Training Optimization
  • 🎨 3D Computer Vision & Graphics
  • 🤖 Reinforcement Learning for Robotics

Current Research:

  • Graph Transformer architectures for human pose estimation
  • Topic-modeled curriculum learning for neural network training
  • Real-time 3D motion capture visualization systems

🤝 Let's Connect & Collaborate

💬 Open to Opportunities In:

Research CollaborationOpen Source ProjectsML Engineering RolesSpeaking Engagements

Portfolio LinkedIn Email ORCID

📫 Get In Touch

def reach_out():
    interests = {
        "collaborate_on": ["Research projects", "Open source ML tools", "Production systems"],
        "discuss_about": ["Computer Vision", "Deep Learning", "3D Graphics", "MLOps"],
        "available_for": ["Technical consulting", "Speaking", "Mentoring", "Code review"]
    }
    
    contact = {
        "email": "reiyo1113@gmail.com",
        "linkedin": "linkedin.com/in/reiyo06",
        "portfolio": "oreiyo.space"
    }
    
    return "Let's build something amazing together! 🚀"

print(reach_out())

🐍 Contribution Snake

github contribution grid snake animation

💭 Random Dev Quote


⭐️ From RyoK3N with 💜

"The best way to predict the future is to invent it." - Alan Kay

Made with Love Open Source

Pinned Loading

  1. coreml-keras3 coreml-keras3 Public

    Jupyter Notebook

  2. MocapViewer3D MocapViewer3D Public

    An interactive 3D/2D motion capture visualization tool that allows real-time manipulation of camera perspectives and skeleton pose viewing. This tool provides simultaneous 3D and 2D projective view…

    Python

  3. GTransformer GTransformer Public

    Python 1

  4. 2DPoseEstimation 2DPoseEstimation Public

    Python

  5. AI-Projects AI-Projects Public

    Jupyter Notebook

  6. Topic-Modeled-Curriculum-Learning-for-Better-Neural-Network-Training Topic-Modeled-Curriculum-Learning-for-Better-Neural-Network-Training Public

    Jupyter Notebook